The Human Visual System and Adversarial AI
arXiv:2001.01172
Abstract
This paper applies theories about the Human Visual System to make Adversarial AI more effective. To date, Adversarial AI has modeled perceptual distances between clean and adversarial examples of images using Lp norms. These norms have the benefit of simple mathematical description and reasonable effectiveness in approximating perceptual distance. However, in prior decades, other areas of image processing have moved beyond simpler models like Mean Squared Error (MSE) towards more complex models that better approximate the Human Visual System (HVS). We demonstrate a proof of concept of incorporating HVS models into Adversarial AI.
References in corpus (9)
- Towards Deep Learning Models Resistant to Adversarial Attacks
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
- Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
- Robust Physical-World Attacks on Deep Learning Models
- Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
- Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
- Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
- Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
- On the Effectiveness of Defensive Distillation