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Anti-forensics for JPEG decompression based on generative adversarial network | IEEE Conference Publication | IEEE Xplore

Anti-forensics for JPEG decompression based on generative adversarial network


Abstract:

Eliminating artifacts left after decompression of JPEG images, especially those compressed at high quality factors, is a challenging issue in image anti-forensics. In thi...Show More

Abstract:

Eliminating artifacts left after decompression of JPEG images, especially those compressed at high quality factors, is a challenging issue in image anti-forensics. In this paper, JPEG decompression anti-forensics are modeled as an image-to-image translation problem, where a generative adversarial network framework is used to translate a JPEG decompressed image to a reconstructed one. Due to the introduction of chroma upsampling artifacts during image decompression, the difference distribution of odd and even pixel pairs on the chroma plane of the uncompressed and decompressed image is different. To solve this issue, we propose an odd-even chroma difference loss to recover this distribution difference. Experimental results show that the modified images generated by this anti-forensic method are able to deceive existing detectors and have excellent visual quality.
Date of Conference: 12-14 April 2024
Date Added to IEEE Xplore: 29 July 2024
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Conference Location: Hangzhou, China
References is not available for this document.

I. Introduction

Image anti-forensics is a technique aimed at modifying or concealing the processing traces of digital images to make them difficult to detect or analyze by digital forensic tools.

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References

References is not available for this document.