Improving Perceptual Quality of Spatially Transformed Adversarial Examples

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2022-8
Aydın, Ayberk
Deep neural networks are known to be vulnerable to additive adversarial perturbations. The amount of these additive perturbations are generally quantified using Lp metrics over the difference between adversarial and benign examples. However, even when the measured perturbations are small, they tend to be noticeable by human observers since Lp distance metrics are not representative of human perception. Spatially transformed examples work by distorting pixel locations instead of applying an additive perturbation or altering the pixel values directly, which produces adversarial examples with improved visual quality. However, the perturbation made by spatial transformations produce visible non-smooth distortions on luminance channels and needs a smoothness regularization over the applied flow field in order to improve the visual quality. On the other hand, humans are less sensitive to changes in chrominance component of visual media and such as resolution loss or pixel shifts in a constrained neighborhood. Motivated by these observations, we propose a novel variation of spatially transformed adversarial examples that creates adversarial examples by applying spatial transformations to chrominance channels of perceptual colorspaces such as Y CbCr and CIELAB to generate adversarial examples with high perceptual quality. Moreover, we find that the visual quality of these examples could be further improved by limiting the magnitude of applied spatial transformations. In a targeted white-box attack setting, the proposed method is able to obtain competitive fooling rates and experimental evaluations show that the proposed method has favorable results in terms of approximate perceptual distance between benign and adversarial images.

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Citation Formats
A. Aydın, “Improving Perceptual Quality of Spatially Transformed Adversarial Examples,” M.S. - Master of Science, Middle East Technical University, 2022.