DeepAI AI Chat
Log In Sign Up

Attacking Adversarial Defences by Smoothing the Loss Landscape

08/01/2022
by   Panagiotis Eustratiadis, et al.
0

This paper investigates a family of methods for defending against adversarial attacks that owe part of their success to creating a noisy, discontinuous, or otherwise rugged loss landscape that adversaries find difficult to navigate. A common, but not universal, way to achieve this effect is via the use of stochastic neural networks. We show that this is a form of gradient obfuscation, and propose a general extension to gradient-based adversaries based on the Weierstrass transform, which smooths the surface of the loss function and provides more reliable gradient estimates. We further show that the same principle can strengthen gradient-free adversaries. We demonstrate the efficacy of our loss-smoothing method against both stochastic and non-stochastic adversarial defences that exhibit robustness due to this type of obfuscation. Furthermore, we provide analysis of how it interacts with Expectation over Transformation; a popular gradient-sampling method currently used to attack stochastic defences.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/20/2020

Towards Understanding the Dynamics of the First-Order Adversaries

An acknowledged weakness of neural networks is their vulnerability to ad...
02/24/2021

Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis

The susceptibility of deep neural networks to untrustworthy predictions,...
01/31/2021

Admix: Enhancing the Transferability of Adversarial Attacks

Although adversarial attacks have achieved incredible attack success rat...
11/05/2020

Sampled Nonlocal Gradients for Stronger Adversarial Attacks

The vulnerability of deep neural networks to small and even imperceptibl...
11/08/2021

Robust and Information-theoretically Safe Bias Classifier against Adversarial Attacks

In this paper, the bias classifier is introduced, that is, the bias part...
03/04/2020

StochasticRank: Global Optimization of Scale-Free Discrete Functions

In this paper, we introduce a powerful and efficient framework for the d...
11/09/2021

Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search

Numerous studies have demonstrated that deep neural networks are easily ...