SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning

TL;DR

This paper introduces SAB, a novel backdoor attack in federated learning that uses steganography to create imperceptible triggers, enhancing attack robustness and evasion of detection methods.

Contribution

The paper presents SAB, a new backdoor attack method utilizing steganographic triggers and advanced gradient updating techniques to improve stealth and longevity in federated learning.

Findings

SAB achieves higher backdoor accuracy with minimal perceptibility.

SAB effectively evades existing backdoor defenses.

The attack demonstrates increased lifespan and generalization in federated learning.

Abstract

Federated learning, an innovative network architecture designed to safeguard user privacy, is gaining widespread adoption in the realm of technology. However, given the existence of backdoor attacks in federated learning, exploring the security of federated learning is significance. Nevertheless, the backdoors investigated in current federated learning research can be readily detected by human inspection or resisted by detection algorithms. Accordingly, a new goal has been set to develop stealing and robust federated learning backdoor attacks. In this paper, we introduce a novel approach, SAB, tailored specifically for backdoor attacks in federated learning, presenting an alternative gradient updating mechanism. SAB attack based on steganographic algorithm, using image steganographic algorithm to build a full-size trigger to improve the accuracy of backdoors and use multiple loss joint computation to produce triggers. SAB exhibits smaller distances to benign samples and greater imperceptibility to the human eye. As such, our triggers are capable of mitigating or evading specific backdoor defense methods. In SAB, the bottom-95\% method is applied to extend the lifespan of backdoor attacks. It updates the gradient on minor value points to reduce the probability of being cleaned. Finally, the generalization of backdoors is enhanced with Sparse-update to improve the backdoor accuracy.