Human Imperceptible Attacks and Applications to Improve Fairness

Abstract

Modern neural networks are able to perform at least as well as humans in numerous tasks involving object classification and image generation. However, small perturbations which are imperceptible to humans may significantly degrade the performance of well-trained deep neural networks. We provide a Distributionally Robust Optimization (DRO) framework which integrates human-based image quality assessment methods to design optimal attacks that are imperceptible to humans but significantly damaging to deep neural networks. Through extensive experiments, we show that our attack algorithm generates better-quality (less perceptible to humans) attacks than other state-of-the-art human imperceptible attack methods. Moreover, we demonstrate that DRO training using our optimally designed human imperceptible attacks can improve group fairness in image classification. Towards the end, we provide an algorithmic implementation to speed up DRO training significantly, which could be of independent interest.

References in corpus (8)

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Countering Adversarial Images using Input Transformations
• A study of the effect of JPG compression on adversarial images
• No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
• Predictive Inequity in Object Detection
• Noise or Signal: The Role of Image Backgrounds in Object Recognition
• Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric
• Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm