Adversarial examples are known to have a negative effect on the performance
of classifiers which have otherwise good performance on undisturbed images.
These examples are generated by adding non-random noise to the testing samples
in order to make classifier misclassify the given data. Adversarial attacks use
these intentionally generated examples and they pose a security risk to the
machine learning based systems. To be immune to such attacks, it is desirable
to have a pre-processing mechanism which removes these effects causing
misclassification while keeping the content of the image. JPEG and JPEG2000 are
well-known image compression techniques which suppress the high-frequency
content taking the human visual system into account. JPEG has been also shown
to be an effective method for reducing adversarial noise. In this paper, we
propose applying JPEG2000 compression as an alternative and systematically
compare the classification performance of adversarial images compressed using
JPEG and JPEG2000 at different target PSNR values and maximum compression
levels. Our experiments show that JPEG2000 is more effective in reducing
adversarial noise as it allows higher compression rates with less distortion
and it does not introduce blocking artifacts.

通过使用 JPEG2000 压缩作为一种替代方法，系统地比较了使用不同目标 PSNR 值和最大压缩级别的 JPEG 和 JPEG2000 压缩的对抗性图像的分类性能，实验表明，JPEG2000 更有效地减少了对抗性噪声，允许更高的压缩率和更少的失真，并且不会引入阻塞伪影。