CN111431796B - Instant messaging early warning method and device, computing equipment and storage medium - Google Patents
Instant messaging early warning method and device, computing equipment and storage medium Download PDFInfo
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- CN111431796B CN111431796B CN202010206314.2A CN202010206314A CN111431796B CN 111431796 B CN111431796 B CN 111431796B CN 202010206314 A CN202010206314 A CN 202010206314A CN 111431796 B CN111431796 B CN 111431796B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/21—Monitoring or handling of messages
- H04L51/216—Handling conversation history, e.g. grouping of messages in sessions or threads
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/04—Real-time or near real-time messaging, e.g. instant messaging [IM]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/30—Network architectures or network communication protocols for network security for supporting lawful interception, monitoring or retaining of communications or communication related information
- H04L63/302—Network architectures or network communication protocols for network security for supporting lawful interception, monitoring or retaining of communications or communication related information gathering intelligence information for situation awareness or reconnaissance
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/30—Network architectures or network communication protocols for network security for supporting lawful interception, monitoring or retaining of communications or communication related information
- H04L63/308—Network architectures or network communication protocols for network security for supporting lawful interception, monitoring or retaining of communications or communication related information retaining data, e.g. retaining successful, unsuccessful communication attempts, internet access, or e-mail, internet telephony, intercept related information or call content
Abstract
The invention discloses an instant messaging early warning method, an instant messaging early warning device, computing equipment and a storage medium, wherein the method comprises the following steps: obtaining at least two chat objects in a chat group, and obtaining friend lists corresponding to the chat objects respectively; counting the number of similar friend head portraits and the number of common friends between chat objects according to the friend list; and monitoring the chat contents in the chat group in real time, and providing early warning information for the target chat objects in the chat group according to the chat contents, the number of the head portraits of the similar friends and the number of the common friends. The embodiment of the invention can rapidly identify whether the current chat object is a suspicious object or a victim object, can provide timely early warning information for the victim object, and reduces the harm of fraud molecules to the society.
Description
Technical Field
The embodiment of the invention relates to information and network security technologies, in particular to an instant messaging early warning method, an instant messaging early warning device, computing equipment and a storage medium.
Background
With the rapid development of the internet industry, the number of events for illegal people to use the internet to make a crime is increasing, and the perfection of information and network security technology is very important.
In recent years, cases of fraud by using chat software to impersonate friends have been common in the internet, the existing practice is not limited to being realized by sending fraud information, and such fraudulent fraudsters usually adopt social engineering, trojan propagation and other methods to acquire a friend list in the chat software of a victim, and then adopt a head portrait or a name which is as same as that of a plurality of friends in the chat software list of the victim to acquire the trust of the victim by establishing a group chat which comprises the head portrait and the name which are as same as that of the plurality of friends of the victim, so as to implement fraud behaviors on the victim.
The committing mode is simple, the amount of fraud money is huge, and the social influence is great. For example, a counterfeit boss swipes financial personnel through a group chat, and the fraudulent use of the financial personnel can directly lead to the bankruptcy of the company.
Disclosure of Invention
The embodiment of the invention provides an instant messaging early warning method, an instant messaging early warning device, computing equipment and a storage medium, which can be used for rapidly identifying whether a current chat object is a suspicious object or a victim object and reducing the harm of fraud molecules to the society.
In a first aspect, an embodiment of the present invention provides an instant messaging early warning method, where the method includes:
Obtaining at least two chat objects in a chat group, and obtaining a friend list corresponding to each chat object;
counting the number of similar friend head portraits and the number of common friends between chat objects according to the friend list;
and monitoring the chat contents in the chat group in real time, and providing early warning information for the target chat objects in the chat group according to the chat contents, the number of the similar friend head portraits and the number of the common friends.
In a second aspect, an embodiment of the present invention further provides an instant messaging early warning device, where the device includes:
the friend list acquisition module is used for acquiring at least two chat objects in the chat group and acquiring friend lists corresponding to the chat objects respectively;
the similar friend head portrait quantity and common friend quantity counting module is used for counting the similar friend head portrait quantity and common friend quantity among the chat objects according to the friend list;
and the early warning information providing module is used for monitoring the chat contents in the chat group in real time and providing early warning information for the target chat objects in the chat group according to the chat contents, the number of the head portraits of the similar friends and the number of the common friends.
In a third aspect, an embodiment of the present invention further provides a computing device, where the computing device includes:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement an instant messaging early warning method provided by any embodiment of the invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements an instant messaging early warning method provided in any embodiment of the present invention.
The embodiment of the invention firstly obtains the friend lists corresponding to at least two chat objects in the chat group, then counts the similar friend head portrait number and the common friend number among the chat objects according to the friend lists, finally monitors the chat contents in the chat group in real time, and provides early warning information for the target chat objects in the chat group according to the chat contents, the similar friend head portrait number and the common friend number. The embodiment of the invention can rapidly identify whether the current chat object is a suspicious object or a victim object, can provide timely early warning information for the victim object, and reduces the harm of fraudsters to the society.
Drawings
Fig. 1 is a flowchart of an instant messaging early warning method according to an embodiment of the present invention;
fig. 2 is a flowchart of an instant messaging early warning method according to a second embodiment of the present invention;
FIG. 3 is a flowchart of a method for counting the number of similar friend avatars in chat objects according to a second embodiment of the present invention;
fig. 4 is a structural diagram of an instant messaging early warning device in a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computing device in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of an instant messaging early warning method provided in an embodiment of the present invention, where this embodiment is applicable to a situation where early warning information is initiated to a fraud object in a process of fraud by using chat software to impersonate friends in the internet, and the method may be executed by an instant messaging early warning device, where the device may be implemented by software and/or hardware, and may be generally integrated in a terminal or a server having data identification and processing functions, and specifically includes the following steps:
In this embodiment, two or more chat objects in the chat group may be present, the chat objects in the chat group in the current chat are obtained by identifying the personal identification corresponding to the chat information, and the buddy list of each chat object is obtained according to the address book of each chat object.
And step 120, counting the number of the similar friend head portraits and the number of the common friends among the chat objects according to the friend list.
In this embodiment, a fraudster can usually easily obtain the avatar information of the friend list of the victim, but can hardly obtain the identification of the friend list, for example, the identification can be an account number used by a user logging in the chat software, and the identification has uniqueness in the chat software. The number of similar friends head portraits among the chat objects can be counted by identifying the head portraits of the friend lists of the chat objects, and the number of common friends among the chat objects can be determined by identifying the identity marks of the friend lists of the chat objects. In a specific example, if the number of similar friends in the friend lists of two chat objects in the chat group is greater than the actual number of common friends, it indicates that there is a possibility of chatting with fake friends by using the head portraits in the chat group.
Specifically, counting the number of similar friend head portraits among chat objects according to the friend list, may include the following steps:
s101, acquiring a friend head portrait set corresponding to each chat object according to the friend list;
in this step, according to the friend list of each chat object, all the friend head portrait pictures in each friend list are taken as a friend head portrait set corresponding to each chat object, for example, two chat objects in a chat group are obtained as a and B, the friend lists corresponding to the chat objects are obtained as a1, a2, A3, B1, B2 and B3, and the friend head portrait sets corresponding to the chat objects are respectively { a1, a2, A3} and { B1, B2, B3 }.
S102, respectively calculating head portrait feature vectors of the head portraits of the friends in the head portrait sets of the friends;
in this step, the hash value of the picture of each friend avatar in the friend avatar set { a1, a2, A3} and { B1, B2, B3} in the above step is calculated by using a hash algorithm, for example, a11, a22, a33, B11, B22 and B33, and the hash value of the picture is used as the avatar feature vector of each friend avatar;
the hash algorithm may be an average hash algorithm, a perceptual hash algorithm, a differential hash algorithm, or the like.
S103, counting the quantity value of the head portrait feature vector meeting the similarity distance condition between every two chat objects, and taking the quantity value as the quantity of the head portraits of the similar friends between every two chat objects.
In this embodiment, the avatar feature vectors of the avatars of the friends in the avatar set of the friends, such as a11, a22, a33, B11, B22, and B33, are already calculated through the above steps, then the hamming distance between the avatar feature vectors of the friends in each group between two chat objects is calculated, if the hamming distance is greater than the preset threshold, the friends in the group are used as similar friends between two chat objects, then the quantity value of the avatar feature vector with the hamming distance greater than the preset threshold is counted, and the quantity value is used as the quantity of the similar avatars between two chat objects.
In this embodiment, the quantity value of the avatar feature vector with the hamming distance greater than 6 is used as the quantity of the similar friend avatars between two chat objects.
Specifically, the counting of the number of common friends between the chat objects according to the friend list may include the following steps:
s201, acquiring a friend identity set corresponding to each chat object according to the friend list;
in this step, according to the buddy list of each chat object, the account number used by the buddy login chat software in each buddy list may be used as the buddy identity, for example, two chat objects in the chat group are obtained as a and B, the buddy lists corresponding to the chat objects are obtained as a1, a2, A3, B1, B2, and B3, and the set of buddy identities corresponding to the chat objects are { a1, a2, A3} and { B1, B2, B3 }.
S202, counting the number value of the identical friend identification in the friend identification set between every two chat objects as the number of the common friends between every two chat objects.
In the above steps, the friend identity sets { a1, a2, a3} and { b1, b2, b3} corresponding to each chat object are determined, and by comparing whether the friend identities of each group between two chat objects are consistent, if so, the group of friends is used as a common friend between two chat objects, then the quantity value of the same friend identity is counted, and the quantity value is used as the quantity of the common friend between two chat objects.
In this embodiment, the chat content in the chat group is obtained in real time, and then it is determined whether the chat content includes preset financial keywords, such as keywords of "money amount", "money transfer", and "financial", and if it is determined that the chat content includes financial keywords, two chat objects in the chat group are taken as a group, and the chat objects in the chat group are divided into a plurality of target groups.
Through the steps, the number of similar friend head portraits and the number of common friends between two chat objects in the target grouping can be calculated, if it is determined that the difference value between the number of similar friend head portraits and the number of common friends between the two chat objects in the target grouping exceeds a preset difference threshold, for example, the number of similar friend head portraits between the two chat objects is greater than 6, and the number of common friends is less than 6, the chat object which firstly sends the financial keywords in the target grouping is identified according to chat information and sending time included in the chat content, the chat object is taken as a suspicious object, another chat user in the target grouping is taken as a target chat object, and then early warning information is provided for the target chat object.
The embodiment of the invention firstly obtains the friend lists corresponding to at least two chat objects in the chat group, then counts the similar friend head portrait number and the common friend number among the chat objects according to the friend lists, finally monitors the chat contents in the chat group in real time, and provides early warning information for the target chat objects in the chat group according to the chat contents, the similar friend head portrait number and the common friend number. The embodiment of the invention can rapidly identify whether the current chat object is a suspicious object or a victim object, can provide timely early warning information for the victim object, and reduces the harm of fraudsters to the society.
Example two
This embodiment is a further refinement of the first embodiment, and the same or corresponding terms as those in the first embodiment are explained, and are not repeated herein.
Fig. 2 is a flowchart of an instant messaging early warning method according to a second embodiment of the present invention, and as shown in fig. 2, the method includes the following steps:
And step 220, counting the number of similar friend head portraits among the chat objects according to the friend list.
In this embodiment, counting the number of similar friend head portraits among chat objects according to the friend list, as shown in fig. 3, may include the following steps:
s201, acquiring a friend head portrait set corresponding to each chat object according to the friend list;
in this step, according to the friend list of each chat object, all the friend head portrait pictures in each friend list are taken as a friend head portrait set corresponding to each chat object, for example, two chat objects in a chat group are obtained as a and B, the friend lists corresponding to the chat objects are obtained as a1, a2, A3, B1, B2 and B3, and the friend head portrait sets corresponding to the chat objects are respectively { a1, a2, A3} and { B1, B2, B3 }.
S202, obtaining a target friend head portrait from a currently processed friend head portrait set, and converting the target friend head portrait into a pixel matrix with a standard size;
in this step, the friend head portrait sets corresponding to the chat objects are respectively obtained as { a1, a2, A3} and { B1, B2, B3}, each friend head portrait in the friend head portrait set is taken as a target friend head portrait, and head portrait pictures of the target friend head portrait can be uniformly scaled into a pixel matrix with the size of 8 × 8.
S203, converting the pixel matrix into a single-channel gray-scale image according to a preset conversion algorithm, and calculating a gray-scale value of each pixel point in the gray-scale image and a gray-scale mean value corresponding to the gray-scale image;
in this embodiment, the 8 × 8 pixel matrix in step S202 is converted into a single-channel gray-scale map by using an average method, then the gray-scale value of each pixel in the gray-scale map is calculated, and the gray-scale average value corresponding to the gray-scale map is calculated according to the gray-scale values of all the pixels in the gray-scale map.
S204, according to the gray value of each pixel point in the gray image and the data difference between the gray average value, carrying out reassignment on the gray value of each pixel point in the gray image to obtain the head portrait feature vector of the target friend head portrait;
In this step, comparing the gray value of each pixel point in the gray-scale image with the gray value of the average, if the gray value of a certain pixel point is greater than the gray value of the average, recording the gray value of the pixel point as 1, otherwise recording the gray value of the pixel point as 0; and reassigning the gray values of all pixel points in the gray map to obtain a picture hash value corresponding to the gray map, and taking the picture hash value as the head portrait feature vector of the head portrait of the target friend.
S205, obtaining avatar feature vectors corresponding to the currently processed first chat object and the second chat object respectively;
in the step, the chat objects in the chat group are grouped in pairs, the chat objects in the group in pairs are used as a first chat object and a second chat object, and the calculation mode of the step is utilized to obtain the avatar feature vectors of the target friend avatars corresponding to the first chat object and the second chat object respectively.
S206, respectively calculating the vector distance between each avatar characteristic vector of the first chat object and each avatar characteristic vector of the second chat object;
in this embodiment, the image hash values, which are the feature vectors of the avatars of the first chat object and the second chat object, are obtained through the above steps, in this step, the image hash values are converted into hexadecimal hash values, then hamming distances between the hexadecimal hash values of the friend avatars of each group of the first chat object and the second chat object are calculated, and the hamming distances are used as vector distances between the feature vectors of the avatars of the first chat object and the feature vectors of the avatars of the second chat object.
And S207, acquiring the quantity value of the head portrait feature vectors with the vector distance exceeding the preset similarity distance as the quantity of the head portraits of the similar friends.
In this step, if the hamming distance is greater than the preset threshold, the group of friends is used as similar friends between the first chat object and the second chat object, then the quantity value of the avatar feature vector with the hamming distance greater than the preset threshold is counted, and the quantity value is used as the quantity of the avatar of the similar friends between the first chat object and the second chat object.
And step 230, counting the number of common friends among the chat objects according to the friend list.
In this embodiment, counting the number of common friends between the chat objects according to the friend list may specifically include the following steps:
s208, acquiring a friend identity set corresponding to each chat object according to the friend list;
in this step, according to the buddy list of each chat object, the account number used by the buddy in each buddy list to log in the chat software can be used as the buddy identity, and all the buddy identities in the buddy list of the chat object together form a buddy identity set corresponding to the chat object.
S209, counting the quantity values of the identical friend identifications in the friend identification set between every two chat objects as the quantity of the common friends between every two chat objects.
In the above steps, the friend identification sets corresponding to the chat objects are determined, and the friend identifications of each group between every two chat objects are compared to determine whether the friend identifications are consistent, if so, the group of friends is used as common friends between every two chat objects, then the quantity value of the identity identifications of the same friends is counted, and the quantity value is used as the quantity of the common friends between every two chat objects.
In this step, the chat content in the chat group is obtained in real time, and it is determined whether the chat content includes preset financial keywords, such as financial keywords like "money amount", "transfer" and "finance", through semantic recognition.
In the above steps, the number of the similar friend head portraits and the number of the common friends between the two chat objects may be calculated, and it is determined whether the difference value between the number of the similar friend head portraits and the number of the common friends between the two chat objects exceeds a preset difference threshold, for example, whether the number of the similar friend head portraits between the two chat objects is greater than 6 and the number of the common friends is less than 6 is determined, if yes, step 260 is executed, and if not, the warning information is not provided.
And step 260, providing early warning information for the target chat object.
In this step, according to the chat information and the sending time included in the chat content, the chat object which sends the financial keywords first among the chat objects is identified, the chat object is used as a suspicious object, another chat user corresponding to the suspicious object is used as a target chat object, and then early warning information is provided for the target chat object.
The embodiment of the invention firstly obtains the friend lists corresponding to at least two chat objects in the chat group, then counts the similar friend head portrait number and the common friend number among the chat objects according to the friend lists, finally monitors the chat contents in the chat group in real time, and provides early warning information for the target chat objects in the chat group according to the chat contents, the similar friend head portrait number and the common friend number. The embodiment of the invention can rapidly identify whether the current chat object is a suspicious object or a victim object, can provide timely early warning information for the victim object, and reduces the harm of fraudsters to the society.
EXAMPLE III
Fig. 4 is a structural diagram of an instant messaging early warning device according to a third embodiment of the present invention, where the device includes: a friend list obtaining module 410, a similar friend head portrait number and common friend number counting module 420 and an early warning information providing module 430.
The buddy list obtaining module 410 is configured to obtain at least two chat objects in a chat group, and obtain a buddy list corresponding to each chat object; a similar friend head portrait number and common friend number counting module 420, configured to count the number of similar friend head portraits and the number of common friends between the chat objects according to the friend list; and the early warning information providing module 430 is configured to monitor chat contents in the chat group in real time, and provide early warning information to a target chat object in the chat group according to the chat contents, the number of the similar friend avatars and the number of the common friends.
According to the technical scheme, the method comprises the steps of firstly obtaining friend lists corresponding to at least two chat objects in a chat group, then counting the number of similar friend head portraits and the number of common friends among the chat objects according to the friend lists, finally monitoring the chat contents in the chat group in real time, and providing early warning information for the target chat objects in the chat group according to the chat contents, the number of the similar friend head portraits and the number of the common friends. The embodiment of the invention can rapidly identify whether the current chat object is a suspicious object or a victim object, can provide timely early warning information for the victim object, and reduces the harm of fraud molecules to the society.
On the basis of the foregoing embodiments, the similar friend head portrait number and common friend number statistics module 420 may include:
a friend head portrait set obtaining unit, configured to obtain, according to the friend list, friend head portrait sets corresponding to the chat objects, respectively;
the head portrait feature vector calculation unit is used for calculating head portrait feature vectors of all the head portraits in all the friend head portrait sets respectively;
the similar friend head portrait quantity determining unit is used for counting the quantity value of head portrait feature vectors which satisfy the similarity distance condition between every two chat objects and are used as the quantity of the similar friend head portraits between every two chat objects;
the target friend head portrait converting unit is used for acquiring a target friend head portrait from a currently processed friend head portrait set and converting the target friend head portrait into a pixel matrix with a standard size;
the pixel matrix conversion unit is used for converting the pixel matrix into a single-channel gray-scale image according to a preset conversion algorithm and calculating the gray-scale value of each pixel point in the gray-scale image and the gray-scale mean value corresponding to the gray-scale image;
the gray value assignment unit is used for reassigning the gray value of each pixel point in the gray map according to the gray value of each pixel point in the gray map and the data difference between the gray average value to obtain the head portrait feature vector of the head portrait of the target friend;
The head portrait feature vector acquisition unit is used for acquiring head portrait feature vectors respectively corresponding to the currently processed first chat object and the second chat object;
a vector distance calculating unit, configured to calculate a vector distance between each avatar feature vector of the first chat object and each avatar feature vector of the second chat object, respectively;
the similar friend head portrait number obtaining unit is used for obtaining the quantity value of head portrait feature vectors with the vector distance exceeding the preset similarity distance as the head portrait number of the similar friends;
a friend identity set obtaining unit, configured to obtain, according to the friend list, a friend identity set corresponding to each of the chat objects;
and the common friend number determining unit is used for counting the number value of the same friend identification in the friend identification set between every two chat objects as the common friend number between every two chat objects.
The warning information providing module 430 may include:
the chat object grouping unit is used for grouping every two chat objects under the condition that the chat contents comprise the financial keywords;
the target chat object determining unit is used for determining a target chat object in a target group under the condition that the difference value between the number of similar friend head portraits and the number of common friends in the target group exceeds a preset difference threshold;
The early warning information providing unit is used for providing early warning information for the target chat object;
and the financial key word identification unit is used for identifying a chat object which firstly sends the financial key word in the target group according to the chat content, and taking another chat user in the target group as the target chat object.
The instant messaging early warning device provided by the embodiment of the invention can execute the instant messaging early warning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 5 is a schematic structural diagram of a computing apparatus according to a fourth embodiment of the present invention, as shown in fig. 5, the computing apparatus includes a processor 510, a memory 520, an input device 530, and an output device 540; the number of processors 510 in the computing device may be one or more, and one processor 510 is taken as an example in fig. 5; the processor 510, memory 520, input device 530, and output device 540 in the computing device may be connected by a bus or other means, such as by a bus in fig. 5.
The memory 520 is a computer-readable storage medium and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to an instant messaging early warning method in the embodiment of the present invention (for example, the buddy list acquisition module 410, the similar buddy avatar count and common buddy count statistics module 420, and the early warning information providing module 430 in an instant messaging early warning apparatus). The processor 510 executes various functional applications and data processing of the computing device by executing software programs, instructions and modules stored in the memory 520, so as to implement an instant messaging early warning method as described above. That is, the program when executed by the processor implements:
Obtaining at least two chat objects in a chat group, and obtaining a friend list corresponding to each chat object;
counting the number of similar friend head portraits and the number of common friends between the chat objects according to the friend list;
and monitoring the chat contents in the chat group in real time, and providing early warning information for the target chat objects in the chat group according to the chat contents, the number of the head portraits of the similar friends and the number of the common friends.
The memory 520 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 520 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 520 can further include memory located remotely from the processor 510, which can be connected to a computing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 530 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the computing device, and may include a keyboard and a mouse, etc. The output device 540 may include a display device such as a display screen.
EXAMPLE five
The fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements an instant messaging early warning method according to any embodiment of the present invention. Of course, the computer-readable storage medium provided in the embodiments of the present invention may perform related operations in the instant messaging early warning method provided in any embodiment of the present invention. That is, the program when executed by the processor implements:
obtaining at least two chat objects in a chat group, and obtaining a friend list corresponding to each chat object;
counting the number of similar friend head portraits and the number of common friends between chat objects according to the friend list;
and monitoring the chat contents in the chat group in real time, and providing early warning information for the target chat objects in the chat group according to the chat contents, the number of the similar friend head portraits and the number of the common friends.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computing device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the instant messaging early warning device, each unit and each module included in the embodiment are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing description is only exemplary of the invention and that the principles of the technology may be employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An instant messaging early warning method is characterized by comprising the following steps:
obtaining at least two chat objects in a chat group, and obtaining a friend list corresponding to each chat object;
counting the number of similar friend head portraits and the number of common friends between chat objects according to the friend list;
and monitoring the chat contents in the chat group in real time, and providing early warning information for the target chat objects in the chat group according to the chat contents, the number of the similar friend head portraits and the number of the common friends.
2. The method of claim 1, wherein counting the number of similar buddies' avatars between chat objects based on the buddy list comprises:
acquiring a friend head portrait set corresponding to each chat object respectively according to the friend list;
respectively calculating head portrait feature vectors of the head portraits of the friends in the head portraits set of the friends;
and counting the quantity values of the head portrait feature vectors between every two chat objects, wherein the quantity values of the head portrait feature vectors meet the similarity distance condition and serve as the quantity of the head portraits of the similar friends between every two chat objects.
3. The method of claim 2, wherein the avatar feature vector of the friend avatar in the friend avatar set is calculated;
acquiring a target friend head portrait from a currently processed friend head portrait set, and converting the target friend head portrait into a pixel matrix with a standard size;
converting the pixel matrix into a single-channel gray-scale image according to a preset conversion algorithm, and calculating a gray-scale value of each pixel point in the gray-scale image and a gray-scale mean value corresponding to the gray-scale image;
and according to the gray value of each pixel point in the gray image and the data difference between the average gray value, carrying out reassignment on the gray value of each pixel point in the gray image to obtain the head portrait feature vector of the head portrait of the target friend.
4. The method of claim 2, wherein counting the quantity value of the avatar feature vector satisfying the similarity distance condition between two chat objects as the quantity of the similar friend avatars between two chat objects comprises:
acquiring avatar feature vectors respectively corresponding to a first chat object and a second chat object which are processed currently;
respectively calculating the vector distance between each avatar characteristic vector of the first chat object and each avatar characteristic vector of the second chat object;
and acquiring the quantity value of the head portrait feature vector with the vector distance exceeding the preset similarity distance as the head portrait quantity of the similar friends.
5. The method of claim 1, wherein counting the number of common friends between chat objects according to the friend list comprises:
acquiring a friend identity identification set corresponding to each chat object respectively according to the friend list;
and counting the quantity values of the identical friend identifications in the friend identification set between every two chat objects as the quantity of the common friends between every two chat objects.
6. The method as claimed in any one of claims 1 to 5, wherein monitoring chat contents in the chat group in real time, and providing warning information to target chat objects in the chat group according to the chat contents, the number of head portraits of similar friends and the number of common friends comprises:
If the chat content comprises the financial keywords, grouping the two chat objects;
if the difference value between the number of the head portraits of the similar friends and the number of the common friends between the two chat objects in the target group exceeds a preset difference threshold, determining the target chat objects in the target group;
and providing early warning information to the target chat object.
7. The method of claim 6, wherein determining a target chat object in the target group comprises:
and according to the chat content, identifying a chat object which firstly sends the financial keywords in the target group, and taking another chat user in the target group as the target chat object.
8. An instant messaging early warning device, comprising:
the friend list acquisition module is used for acquiring at least two chat objects in the chat group and acquiring friend lists corresponding to the chat objects respectively;
the similar friend head portrait quantity and common friend quantity counting module is used for counting the similar friend head portrait quantity and common friend quantity among the chat objects according to the friend list;
and the early warning information providing module is used for monitoring the chat contents in the chat group in real time and providing early warning information for the target chat objects in the chat group according to the chat contents, the number of the head portraits of the similar friends and the number of the common friends.
9. A computing device, wherein the computing device comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement an instant messaging alert method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements an instant messaging alert method according to any one of claims 1 to 7.
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