Mislgan: An Anti-Forensic Camera Model Falsification Framework Using A Generative Adversarial Network

@article{Chen2018MislganAA,
  title={Mislgan: An Anti-Forensic Camera Model Falsification Framework Using A Generative Adversarial Network},
  author={Chen Chen and Xinwei Zhao and Matthew C. Stamm},
  journal={2018 25th IEEE International Conference on Image Processing (ICIP)},
  year={2018},
  pages={535-539}
}
  • Chen Chen, Xinwei Zhao, Matthew C. Stamm
  • Published in
    25th IEEE International…
    2018
  • Computer Science
  • Deep learning techniques have become popular for performing camera model identification. To expose weaknesses in these methods, we propose a new anti-forensic framework that utilizes a generative adversarial network (GAN) to falsify an image's source camera model. Our proposed attack uses the generator trained in the GAN to produce an image that can fool a CNN-based camera model identification classifier. Moreover, our proposed attack will only introduce a minimal amount of distortion to the… CONTINUE READING

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