A Face Data Identity De-recognition Method Based on Generative Adversarial Networks

A recognition method and generative technology, applied in character and pattern recognition, biological neural network models, neural learning methods, etc., can solve problems such as loss of original face information and image quality degradation, and achieve high image quality.

Active Publication Date: 2022-03-11
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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AI Technical Summary

Problems solved by technology

However, the disturbance of random noise will cause the image quality to degrade, and the face-changing technology will be at the cost of completely losing the original face information.

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  • A Face Data Identity De-recognition Method Based on Generative Adversarial Networks
  • A Face Data Identity De-recognition Method Based on Generative Adversarial Networks
  • A Face Data Identity De-recognition Method Based on Generative Adversarial Networks

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Embodiment Construction

[0048] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0049] This application discloses a face data identity de-identification method based on a generative confrontation network, the flow chart of which is as followsfigure 1 As shown, the de-identification method includes the following steps:

[0050] Step 1: Build an image encoding-generating network.

[0051] The image encoding-generating network is used to encode and decouple the face attributes and expression poses of the face image in the latent space. The face image includes the face attribute image and the expression pose image.

[0052] Such as figure 2 As shown, it specifically includes the following sub-steps:

[0053] Step 11: Build the model framework of image encoding-generating network, including:

[0054] The pre-trained ResNet-50 network is used as the face attribute encoding network, and the ResNet-50 network is pre-trained...

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Abstract

The invention discloses a face data identity de-recognition method based on a generative confrontation network, and relates to the technical field of biometric feature recognition and artificial intelligence security. The attribute feature encoding and expression pose encoding of the face image are combined to obtain the first latent vector, the second latent vector is obtained through the mapping network, and the second latent vector is sent to the generation network to obtain the output image, and the face image attribute feature and expression are completed. The fusion of poses, using face-changing technology, realizes the identity de-identification of human vision; secondly, constructs an adversarial vector mapping network, takes the second hidden vector and inputs it into the adversarial vector mapping network to obtain the adversarial hidden vector, and then obtains face recognition through the generation network For adversarial sample images with large gaps in model recognition results and small gaps in human vision, the identity de-identification of the face recognition model can be realized through adversarial sample technology.

Description

technical field [0001] The invention relates to the technical fields of biological feature recognition and artificial intelligence security, in particular to a face data identity de-recognition method based on a generative confrontation network. Background technique [0002] In the era of artificial intelligence based on big data training, computer vision technology is widely used in security and tracking task scenarios, but the resulting security problems of misuse of face data have aroused people's concern about the necessity of protecting face privacy. Pay attention to. Traditional anonymized face technologies, such as mosaicing or blurring faces, have the disadvantage of large information loss, making it impossible for users or data developers to effectively use the anonymized face data. With the introduction of the concepts of adversarial samples and generative adversarial networks, two face recognition (De-identify, De-id) technologies represented by adding random noi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V40/16G06V10/44G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/168G06V40/172G06V10/44G06N3/048G06N3/045G06F18/241
Inventor 杨嵩林程月华
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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