WO2023149513A1 - 偽造画像検出装置、偽造画像検出方法、及びプログラム - Google Patents

偽造画像検出装置、偽造画像検出方法、及びプログラム Download PDF

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WO2023149513A1
WO2023149513A1 PCT/JP2023/003431 JP2023003431W WO2023149513A1 WO 2023149513 A1 WO2023149513 A1 WO 2023149513A1 JP 2023003431 W JP2023003431 W JP 2023003431W WO 2023149513 A1 WO2023149513 A1 WO 2023149513A1
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image data
image
forged
counterfeit
machine learning
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French (fr)
Japanese (ja)
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俊彦 山崎
楓 塩原
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University of Tokyo NUC
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University of Tokyo NUC
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • the present invention relates to a counterfeit image detection device, a counterfeit image detection method, and a program.
  • Non-Patent Document 1 A method for detecting forgery by deep fake has been studied, and for example, a deep learning-based detection method has been developed (for example, Non-Patent Document 1).
  • the present invention has been made in view of the above circumstances, and one of its objects is to provide a counterfeit image detection device, a counterfeit image detection method, and a program capable of detecting various counterfeit images.
  • a counterfeit image detection device that detects a counterfeit image, comprising a processor and a memory, wherein the processor accepts original image data to be subjected to machine learning. is stored in the memory, the original image data stored in the memory is duplicated to generate first and second duplicate image data, and at least one of the generated first and second duplicate image data is applying a predetermined data augmentation process to source image data and target image data, respectively, generating at least one forged image data using the generated target image data and source image data, and generating the original image data as a non-forgery image, and the generated forgery image data as a forgery image, respectively, are input to a predetermined machine learning model, and the machine learning model is machine-learned so that the non-forgery image and the forgery image can be distinguished. , the machine learning model subjected to machine learning is subjected to the process of detecting the forged image.
  • various forged images can be detected regardless of the domain of the dataset used for learning.
  • FIG. 1 is a block diagram showing a configuration example of a forged image detection device according to an embodiment of the present invention
  • FIG. 4 is a functional block diagram showing an example of a control unit that performs machine learning processing of the forged image detection device according to the embodiment of the present invention
  • FIG. 3 is a functional block diagram showing an example of a control unit that performs discrimination processing of the forged image detection device according to the embodiment of the present invention
  • FIG. 4 is a flow chart showing an operation example of the forged image detection device according to the embodiment of the present invention
  • FIG. 4 is an explanatory diagram showing an example of image processing by the operation of the forged image detection device according to the embodiment of the present invention
  • a forged image detection apparatus 1 is a general computer device including a control unit 11, a storage unit 12, an operation unit 13, a display unit 14, and a communication unit 15, as illustrated in FIG. It can be realized by
  • control unit 11 is a processor such as a CPU, and operates according to a program stored in the storage unit 12.
  • the control unit 11 receives original image data to be machine-learned and duplicates the original image data to generate first and second duplicate image data.
  • the control unit 11 applies a predetermined data extension process to at least one of the generated first and second duplicate image data, sets them as source image data and target image data, and divides them into target image data and At least one counterfeit image data is generated using the source image data and the original image data as a non-counterfeit image.
  • the control unit 11 inputs the thus generated forged image data as a forged image into a predetermined machine learning model, and machine-learns the machine learning model so that the non-forged image and the forged image can be distinguished. Then, the control unit 11 subjects the machine learning model subjected to the machine learning to a process of detecting a forged image. A detailed operation of the control unit 11 will be described later.
  • the storage unit 12 is a memory device, disk device, or the like, and holds programs executed by the control unit 11 .
  • This program may be stored and provided in a computer-readable and non-temporary recording medium, and may be copied to this storage unit 12 .
  • the storage unit 12 also operates as a work memory for the control unit 11, and holds original image data, replicated image data, machine learning models, and the like.
  • the operation unit 13 is a mouse, a keyboard, or the like, and accepts user's operations and outputs information representing the contents of the operations to the control unit 11 .
  • the display unit 14 is a display device or the like, and outputs information according to instructions input from the control unit 11 .
  • the communication unit 15 is a network interface or the like, and transmits and receives various data via a network according to instructions input from the control unit 11 .
  • control unit 11 executes a program stored in the storage unit 12 to execute machine learning processing and discrimination processing.
  • control unit 11 that performs machine learning processing includes an original image receiving unit 21, a duplicate image generating unit 22, a data extension processing unit 23, a forged image data generating unit 24, and a machine learning unit 24, as illustrated in FIG.
  • a learning processing unit 25 is functionally included.
  • the control unit 11 that performs discrimination processing functionally includes a target image receiving unit 31, a discrimination processing unit 32, and an output unit 33, as illustrated in FIG.
  • the original image receiving unit 21 receives original image data to be machine-learned.
  • the original image receiving unit 21 normally receives a plurality of original image data to be subjected to machine learning via the communication unit 15, for example.
  • the original image data is image data including an image portion to be forged, such as a person's face portion.
  • the original image receiving unit 21 stores the received original image data in the storage unit 12 .
  • the original image receiving unit 21 may resize the input image data to a predetermined size and store it in the storage unit 12 as original image data.
  • the resizing process here includes at least one process such as image data enlargement/reduction processing and processing for cutting out a predetermined attention area (for example, a portion where a face image is captured) to the input image data. is performed.
  • the duplicated image generation unit 22 selects each piece of original image data stored in the storage unit 12 as a target of sequential processing, duplicates the selected original image data to be processed, and generates first and second duplicated images. Generate data.
  • the data extension processing unit 23 applies predetermined data extension processing to at least one of the first and second duplicate image data generated by the duplicate image generation unit 22 .
  • the data augmentation process is preferably a process of simulating traces of forgery appearing in the forged image when the image is forged.
  • examples of such traces of counterfeiting include: (a) In order to synthesize the face of another person with the image of the original person, a feature point defect ( landmark mismatch), (b) a visual blending boundary; (c) color mismatch due to differences in exposure between the original image and the counterfeit image, and differences in skin tone; (d) frequency inconsistency in image quality (frequency due to encoding, etc.); and so on.
  • the data extension processing unit 23 selects one of the first and second duplicated image data by a predetermined method, for example, predetermined duplicated image data, or randomly selects one of the first and second duplicated image data. Select any copy image data. Then, color conversion processing and frequency conversion processing are applied as data extension processing to one of the selected first and second duplicate image data.
  • This color conversion processing includes a process of randomly changing the value of each RGB channel (RGB shift), a process of randomly changing hue, saturation, and brightness (HueSaturationValue), and a process of randomly changing brightness and contrast ( RandomBrightnessContrast), etc. The parameters for each process are appropriately determined experimentally.
  • the frequency conversion process for example, one is randomly selected from the process of downscaling and then upscaling to reduce the image quality (Downscale), the sharpening process (Sharpen), etc., and is applied alternatively. It is good as a thing.
  • the data extension processing unit 23 outputs either the first or second copy image data, to which data extension processing is applied to at least one, as source image data, and outputs the other as target image data.
  • which is the source image data and which is the target image data may be determined in advance, or uniform random numbers may be generated to differ for each sample (original image data).
  • the forged image data generation unit 24 generates at least one piece of forged image data using the source image data and the target image data output by the data extension processing unit 23 .
  • the source image data Is and the target image data It are respectively assigned reference numerals for easy distinction.
  • the forged image data generation unit 24 identifies image portions to be forged for each of the source image data Is and the target image data It.
  • the image portion to be forged may be each part of the human body such as the face (head), hand, body, and leg of the person. Note that these are only examples, and other parts may be the target of forgery. For the sake of explanation, the case where a person's face is to be forged will be taken as an example below.
  • the forged image data generation unit 24 inputs the original image data, which are the sources of the source image data Is and the target image data It, to the facial feature point extractor.
  • the feature point extractor for the face part a widely known one such as an extractor using Haar-like features can be used. Identify regions that contain
  • the forged image data generation unit 24 obtains a convex area of feature points of the face extracted from the original image data that is the source of the source image data Is and the target image data It (ConvexHull).
  • the forged image data generation unit 24 obtains a mask image M in which the pixels in the convex region obtained here have a predetermined pixel value (for example, "1"), and extracts the image in the mask image M from the source image data Is. and the area (1-M) other than the mask image M extracted from the target image data It are combined to generate the forged image ISB:
  • the machine learning model is a well-known classification model such as EfficientNet-b4 (EFNB4: Mingxing Tan and Quoc Le, Efficient: Rethinking model scaling for convolutional neural networks, In ICML, pp. 6105-6114, 2019). utensils can be used.
  • EFNB4 Mingxing Tan and Quoc Le
  • Efficient Rethinking model scaling for convolutional neural networks, In ICML, pp. 6105-6114, 2019.
  • utensils can be used.
  • the label tj is "0" if xj is any of the non-forgery images I_i, and "1" if xj is any of the forgery images ISB_i.
  • the machine learning processing unit 25 uses this image permutation (training image data set) X and the corresponding label T to convert the classification model F( ⁇ ) into a cross entropy loss L: to optimize.
  • F(x) represents the probability that image x is a forged image.
  • the optimization method a widely known method such as back propagation, which is a general machine learning process, can be employed.
  • the label tj is "0" if xj is any of the non-forgery images It_i, and is "1" if xj is any of the forgery images ISB_i.
  • the target image receiving unit 31 receives input of image data to be determined.
  • the target image receiving unit 31 may use the input image data as target image data T by resizing the input image data to a predetermined size.
  • the resizing process here includes at least one process such as image data enlargement/reduction processing and processing for cutting out a predetermined attention area (for example, a portion where a face image is captured) to the input image data. is performed.
  • the discrimination processing unit 32 inputs the target image data T into the classification model F( ⁇ ), which is a machine learning model that has undergone machine learning through the above-described machine learning process. The determination processing unit 32 then determines whether or not the target image data T is forged image data based on the output of this classification model F(T).
  • the output of the classification model F(T) represents the probability pT that the target image data T is forged image data (0 ⁇ pT ⁇ 1). Therefore, when the output F(T) exceeds a predetermined threshold value (for example, 1/2), the determination processing unit 32 outputs information indicating that the target image data is forged image data. outputs information indicating that the target image data is not forged image data.
  • This threshold can be set arbitrarily, for example experimentally, within the range of values of the output F(T) (not including the boundaries).
  • the output unit 33 outputs information indicating whether or not the target image data is forged image data output by the determination processing unit 32 to the display unit 14 or the like, and presents it to the user.
  • the discrimination processing unit 32 may directly output the output of the classification model F(T) to the output unit 33, output it to the output unit display unit 14, etc., and present it to the user.
  • the forged image detection apparatus 1 of the present embodiment basically has the configuration described above, and operates as follows.
  • the forged image detection apparatus 1 performs machine learning for the purpose of detecting a forged image by synthesizing another person's face with an image of a person's face. .
  • a plurality of image data in which a person's face is captured are prepared in advance as original image data, and are sequentially input to the counterfeit image detection device 1 .
  • the forged image detection device 1 starts the processing illustrated in FIG. 4 and performs the following processing for each original image data I_i. That is, the forged image detection apparatus 1 duplicates the original image data I_i to be processed to generate first and second duplicated image data IR1_i and IR2_i (S11, FIG. 5: S21). For at least one of the first and second replicated image data IR1_i and IR2_i generated in step S11 (here both the first and second replicated image data IR1_i and IR2_i), the forged image detection apparatus 1 detects Data extension processing including color conversion processing and frequency conversion processing is applied (S12, FIG. 5: S22).
  • the color conversion process performed in step S12 includes a process of randomly changing the value of each RGB channel (RGB shift), a process of randomly changing hue, saturation, and brightness (HueSaturationValue), and a process of randomly changing brightness and contrast. RandomBrightnessContrast), and here, each of these processes is performed sequentially. It should be noted that the parameters for each process are randomly determined within a range determined experimentally in advance.
  • the forged image detection device 1 performs color conversion processing and frequency conversion processing on each of the first and second duplicate image data IR1_i and IR2_i based on mutually different parameters.
  • the forged image detection device 1 sets one of the first and second duplicated image data IR1_i and IR2_i after the data extension processing as the source image data Is and the other as the target image data It (S13).
  • the source image data Is and which is the target image data It is made different for each sample (original image data) by generating a uniform random number.
  • the forged image detection device 1 identifies a face image portion as a part of the imaged area of the original image data I_i processed in step S11 (S14, FIG. 5: S23). This processing can be performed by inputting the original image data I_i into a well-known face feature point extractor and detecting the positions where the feature points of each part of the face are captured.
  • the forged image detection device 1 obtains a polygonal area (convex area) including the identified face image portion as the mask image M (S15, FIG. 5: S24).
  • the forged image detection apparatus 1 extracts an image inside the mask image M obtained in step S15 from the source image data Is, and synthesizes it with the area (1-M) other than the mask image M extracted from the target image data It.
  • a forged image ISB_i is generated (S16, FIG. 5: S25).
  • One of the characteristics of this embodiment is that different image processing is applied to one piece of original image data (or one of them may not be subjected to image processing) when generating a forged image. It is to generate a pair of image data as a target and a source and synthesize them to obtain counterfeit image data. Synthesizing the target and the source obtained from the same image in this way generally results in forged image data that is difficult to identify. Forged image data can be obtained.
  • the counterfeit image detection device 1 performs the above processing for each piece of original image data I_i, obtains at least one counterfeit image ISB_i from each, and stores them in the storage unit 12 .
  • the forged image detection device 1 machine-learns a predetermined machine-learning model (for example, EfficientNet-b4 (EFNB4)) using this training image data set (S18).
  • EFNB4 EfficientNet-b4
  • the forged image detection device 1 uses the training image data set X and the corresponding label T to convert the classification model F( ⁇ ) into a cross entropy loss L: to optimize.
  • F(x) represents the probability that image x is a forged image.
  • the optimization method can be performed by adopting a widely known method such as the back propagation method, so the explanation is omitted here.
  • whether or not the image data to be processed is the forgery image data is determined as follows. to decide.
  • the user inputs image data to be determined to the forged image detection device 1 .
  • the counterfeit image detection apparatus 1 receives this image data, performs predetermined preprocessing such as resizing processing, and obtains target image data T.
  • the forged image detection apparatus 1 inputs the target image data T to the classification model F( ⁇ ), which is a machine learning model that has been machine-learned by the above-described machine-learning process. Since the output of this classification model F(T) represents the probability pT that the target image data T is counterfeit image data (0 ⁇ pT ⁇ 1), the counterfeit image detection device 1 displays this output F(T) as it is. It is displayed on the unit 14 and presented to the user. The user refers to this probability to determine whether or not the input image data is forged image data.
  • the forged image detection apparatus 1 of the present embodiment can also perform processing for determining whether or not moving image data is a forged image.
  • the machine learning process may be as described above.
  • the forged image detection device 1 obtains the probability pT that the image data of each frame is forged image data, and further, for example, the average of the probability that the image data corresponding to each frame is forged image data.
  • Generates and displays statistical calculation results for The user can refer to the statistical calculation result presented by the forged image detection device 1 to determine whether the moving image data is a forged image.
  • the forged image detection apparatus 1 of the present embodiment further performs a predetermined data expansion process on the mask image M generated in the process of the forged image data generation unit 24 (the process of step S15 illustrated in FIG. 4). good too.
  • the data expansion processing for the mask image can be, for example, resizing processing, deformation processing, scaling processing, and the like.
  • the forged image detection apparatus 1 detects a characteristic portion of a face image as a partial area captured in the original image data I_i, and randomly selects a portion of the characteristic portion. After invalidation, a polygonal area (convex area) including the characteristic portion of the face image excluding the invalidated characteristic portion is obtained as the initial mask image Ms. Then, a predetermined elastic deformation process (Elastic Deformation) is applied to this initial mask image Ms.
  • This elastic deformation process can adopt the method disclosed in Tianchen Zhao, et al., Learning self-consistency for deepfake detection, In ICCV, pp. 15023-15033, 2021, so detailed description is omitted here. .
  • a plurality of Gaussian filters are sequentially applied to the mask image that has undergone the elastic deformation processing to perform enlargement/reduction processing (at this time, at least a portion of the mask image is pixels are set to values between 0 and 1). Furthermore, using a randomly determined constant r, the pixel values included in the mask image are multiplied by r to obtain the mask image M to be actually used.
  • the constant r is a value greater than 0 and less than or equal to 1, and may be determined by uniformly sampling from among 0.25, 0.5, 0.75, and 1.
  • the ratio of synthesis between the source image and the target image can be diversified, and the generated forged image data can be diversified.
  • the forged image detection apparatus 1 of the present embodiment further extracts the mask image from the source image data Is to which image processing including at least one of resizing, translation, rotation, brightness change, saturation change, and hue change is applied.
  • Forged image data may be obtained by extracting the portion extracted by M and synthesizing it with the portion (1-M) extracted from the target image data other than the mask image M.
  • the forged image detection apparatus 1 performs not only the above-mentioned examples but also resizing, translation, rotation, and color conversion processing (brightness change, saturation change,
  • the source image data Is (or the target image data It) may be generated by applying image processing including at least one of hue change) and frequency conversion processing.
  • JPEG Joint Picture Experts Group
  • 1 forged image detection device 11 control unit, 12 storage unit, 13 operation unit, 14 display unit, 15 communication unit, 21 original image reception unit, 22 duplicate image generation unit, 23 data extension processing unit, 24 forged image data generation unit , 25 machine learning processing unit, 31 target image receiving unit, 32 discrimination processing unit, and 33 output unit.

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