Content-Adaptive Pixel Discretization to Improve Model Robustness
This work provides an incremental improvement in adversarial robustness for deep learning models, particularly for image classification tasks, by extending the applicability of pixel discretization defenses to more complex datasets.
This paper addresses the ineffectiveness of pixel discretization as an adversarial defense on complex datasets. The authors propose Essential Features, a content-adaptive pixel discretization method that uses a per-image adaptive codebook, optionally combined with adaptive blurring, to improve model robustness against L2 and L-infinity adversarial attacks on a wider range of datasets where fixed codebooks previously failed.
Preprocessing defenses such as pixel discretization are appealing to remove adversarial attacks due to their simplicity. However, they have been shown to be ineffective except on simple datasets like MNIST. We hypothesize that existing discretization approaches failed because using a fixed codebook for the entire dataset limits their ability to balance image representation and codeword separability. We first formally prove that adaptive codebooks can provide stronger robustness guarantees than fixed codebooks as a preprocessing defense on some datasets. Based on that insight, we propose a content-adaptive pixel discretization defense called Essential Features, which discretizes the image to a per-image adaptive codebook to reduce the color space. We then find that Essential Features can be further optimized by applying adaptive blurring before the discretization to push perturbed pixel values back to their original value before determining the codebook. Against adaptive attacks, we show that content-adaptive pixel discretization extends the range of datasets that benefit in terms of both L_2 and L_infinity robustness where previously fixed codebooks were found to have failed. Our findings suggest that content-adaptive pixel discretization should be part of the repertoire for making models robust.