Anonymizing digital images utilizing a generative neural network

Patent metadata

Field	Value
Publication number	US-2025284848-A1
Application number	US-202519217725-A
Country	US
Kind code	A1
Filing date	May 23, 2025
Priority date	Nov 2, 2022
Publication date	Sep 11, 2025
Grant date	—

Abstract

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating anonymized digital images utilizing a face anonymization neural network. In some embodiments, the disclosed systems utilize a face anonymization neural network to extract or encode a face anonymization guide that encodes face attribute features, such as gender, ethnicity, age, and expression. In some cases, the disclosed systems utilize the face anonymization guide to inform the face anonymization neural network in generating synthetic face pixels for anonymizing a digital image while retaining attributes, such as gender, ethnicity, age, and expression. The disclosed systems learn parameters for a face anonymization neural network for preserving face attributes, accounting for multiple faces in digital images, and generating synthetic face pixels for faces in profile poses.

First claim

What is claimed is:

1 . A non-transitory computer readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations comprising:
encoding, from a digital image depicting a face of a person, a face anonymization guide comprising features representing one or more of gender, ethnicity, age, or expression associated with the face in the digital image; generating a masked digital image from the digital image by masking face pixels for the face depicted in the digital image; and generating, from the masked digital image utilizing a generative neural network, an anonymized digital image by inpainting a synthetic face within the digital image according to the face anonymization guide.

2 . The non-transitory computer readable medium of claim 1 , wherein inpainting the synthetic face within the digital image is performed without copying or blending pixels originating from another digital image.

3 . The non-transitory computer readable medium of claim 1 , wherein inpainting the synthetic face within the digital image comprising replacing masked face pixels of the masked digital image with synthetic face pixels forming the synthetic face.

4 . The non-transitory computer readable medium of claim 1 , wherein inpainting the synthetic face within the digital image according to the face anonymization guide comprises generating the synthetic face to preserve the one or more of gender, ethnicity, age, or expression associated with the face in the digital image.

5 . The non-transitory computer readable medium of claim 1 , wherein generating the anonymized digital image comprises:
extracting a masked image vector from the masked digital image; and combining the masked image vector and the face anonymization guide utilizing the generative neural network.

6 . The non-transitory computer readable medium of claim 1 , wherein generating the anonymized digital image comprises synthesizing synthetic face pixels utilizing a decoder of the generative neural network according to the face anonymization guide.

7 . The non-transitory computer readable medium of claim 1 , wherein encoding the face anonymization guide comprises:
utilizing a first encoder of the generative neural network to extract a first feature set and a second encoder of the generative neural network to extract a second feature set from the digital image; and combining the first feature set and the second feature set into a guide feature set.

8 . A system comprising:
one or more memory devices storing a generative neural network and a digital image depicting a face of a person; and one or more processors configured to cause the system to anonymize the digital image depicting the face of the person by:
generating a face anonymization guide by encoding features representing one or more of gender, ethnicity, age, or expression of the face in the digital image; and
generating, utilizing the generative neural network, an anonymized digital image by inpainting a synthetic face within the digital image according to the face anonymization guide that preserves the one or more of gender, ethnicity, age, or expression of the face in the digital image.

9 . The system of claim 8 , wherein inpainting the synthetic face within the digital image is performed without copying or blending pixels originating from another digital image.

10 . The system of claim 8 , wherein generating the face anonymization guide comprises utilizing a first encoder of the generative neural network to encode the features.

11 . The system of claim 10 , wherein generating the face anonymization guide comprises:
utilizing a second encoder of the generative neural network to encode additional features of the face depicted in the digital image; and combining the features from the first encoder and the additional features from the second encoder into the face anonymization guide.

12 . The system of claim 8 , wherein generating the anonymized digital image comprises:
generating a masked digital image from the digital image by masking face pixels for the face depicted in the digital image; extracting a masked image vector from the masked digital image utilizing the generative neural network; combining the masked image vector and the face anonymization guide to generate a combined image mask-feature guide vector; and generating the anonymized digital image utilizing a decoder of the generative neural network from the combined image mask-feature guide vector.

13 . The system of claim 12 , wherein generating the masked digital image comprises:
detecting an additional face depicted in the digital image in addition to the face of the person; removing pixels of the additional face to remove the additional face from a boundary around the face depicted in the digital image; and masking the face pixels for the face depicted within the boundary.

14 . The system of claim 12 , wherein generating the masked digital image comprises:
generating a boundary around the face depicted in the digital image; aligning the boundary to orient the face pixels by removing tilt; and masking the face pixels aligned within the boundary.

15 . A computer-implemented method comprising:
encoding, from a digital image depicting a face of a person, a face anonymization guide comprising features representing one or more of gender, ethnicity, age, or expression associated with the face in the digital image; generating a masked digital image from the digital image by masking face pixels for the face depicted in the digital image; and generating, from the masked digital image utilizing a generative neural network, an anonymized digital image by inpainting a synthetic face within the digital image according to the face anonymization guide.

16 . The computer-implemented method of claim 15 , wherein encoding the face anonymization guide comprises generating a feature vector representing gender, ethnicity, age, and expression for the face in the digital image.

17 . The computer-implemented method of claim 15 , wherein encoding the face anonymization guide comprises:
utilizing an encoder of the generative neural network to encode texture features for the face depicted in the digital image; and combining the features representing one or more of gender, ethnicity, age, or expression and the texture features into the face anonymization guide.

18 . The computer-implemented method of claim 15 , wherein generating the masked digital image comprises:
detecting an additional face depicted in the digital image in addition to the face of the person; removing pixel values for pixels of the additional face in the digital image; generating a boundary for the face depicted in the digital image including pixels with removed pixel values for the additional face; and masking the face pixels for the face depicted within the boundary.

19 . The computer-implemented method of claim 15 , wherein generating the anonymized digital image comprises:
extracting a masked image vector from the masked digital image utilizing an encoder of the generative neural network; generating a mapped noise vector by extracting features from a noise vector utilizing a mapper associated with the generative neural network; and synthesizing the synthetic face from the face anonymization guide, the masked image vector, and the mapped noise vector utilizing a decoder of the generative neural network.

20 . The computer-implemented method of claim 15 , whe

Assignees

• Adobe Inc

Inventors

• Khodadadeh Siavash
• Kalarot Ratheesh
• Ghadar Shabnam
• Hold-Geoffroy Yannick

Classifications

• G06N3/0475

  Generative networks · CPC title

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title