ExS-GAN : Synthesizing Anti-Forensics Images via Extra Supervised GAN

Feng DING, Zhangyi SHEN, Guopu ZHU, Sam KWONG, Yicong ZHOU, Siwei LYU

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)


So far, researchers have proposed many forensics tools to protect the authenticity and integrity of digital information. However, with the explosive development of machine learning, existing forensics tools may compromise against new attacks anytime. Hence, it is always necessary to investigate anti-forensics to expose the vulnerabilities of forensics tools. It is beneficial for forensics researchers to develop new tools as countermeasures. To date, one of the potential threats is the generative adversarial networks (GANs), which could be employed for fabricating or forging falsified data to attack forensics detectors. In this article, we investigate the anti-forensics performance of GANs by proposing a novel model, the ExS-GAN, which features an extra supervision system. After training, the proposed model could launch anti-forensics attacks on various manipulated images. Evaluated by experiments, the proposed method could achieve high anti-forensics performance while preserving satisfying image quality. We also justify the proposed extra supervision via an ablation study.
Original languageEnglish
JournalIEEE Transactions on Cybernetics
Publication statusE-pub ahead of print - 20 Oct 2022
Externally publishedYes


  • Anti-forensics
  • digital forensics
  • Digital forensics
  • Forensics
  • generative adversarial network (GAN)
  • Generative adversarial networks
  • Generators
  • Image forensics
  • machine learning
  • Training
  • Transform coding


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