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arxiv 1608.00853 v1 pith:IZNS2GUR submitted 2016-08-02 cs.CV cs.LG

A study of the effect of JPG compression on adversarial images

Gintare Karolina Dziugaite , Zoubin Ghahramani , Daniel M. Roy This is my paper
classification cs.CV cs.LG
keywords imagesadversarialclassificationcompressioneffectdatahumansimage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Neural network image classifiers are known to be vulnerable to adversarial images, i.e., natural images which have been modified by an adversarial perturbation specifically designed to be imperceptible to humans yet fool the classifier. Not only can adversarial images be generated easily, but these images will often be adversarial for networks trained on disjoint subsets of data or with different architectures. Adversarial images represent a potential security risk as well as a serious machine learning challenge---it is clear that vulnerable neural networks perceive images very differently from humans. Noting that virtually every image classification data set is composed of JPG images, we evaluate the effect of JPG compression on the classification of adversarial images. For Fast-Gradient-Sign perturbations of small magnitude, we found that JPG compression often reverses the drop in classification accuracy to a large extent, but not always. As the magnitude of the perturbations increases, JPG recompression alone is insufficient to reverse the effect.

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