Corpus ID: 220514262

Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack

@article{Cheng2020PasadenaPA,
  title={Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack},
  author={Yupeng Cheng and Q. Guo and Felix Juefei-Xu and Xiaofei Xie and Shang-Wei Lin and W. Lin and W. Feng and Yang Liu},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.07097}
}
  • Yupeng Cheng, Q. Guo, +5 authors Yang Liu
  • Published 2020
  • Computer Science, Engineering
  • ArXiv
  • Image denoising techniques have been widely employed in multimedia devices as an image post-processing operation that can remove sensor noise and produce visually clean images for further AI tasks, e.g., image classification. In this paper, we investigate a new task, adversarial denoise attack, that stealthily embeds attacks inside the image denoising module. Thus it can simultaneously denoise input images while fooling the state-of-the-art deep models. We formulate this new task as a kernel… CONTINUE READING

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    Publications referenced by this paper.
    SHOWING 1-10 OF 48 REFERENCES
    Ultrasound evaluation of abdominal pregnancy.
    16
    Three-dimensional printing of chemotherapeutic and antibiotic eluting fibers, seeds, and discs for localized drug delivery in cutaneous disease
    • 2015
    Bi-Directional Cascade Network for Perceptual Edge Detection
    37