Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers

2025-02-07

AI Summary

This work uncovers the intrinsic mechanism underlying the representational fragility of Vision Transformers (ViTs): minute input perturbations induce severe representation fluctuations in middle-to-late layers, leading to semantic inconsistency and degraded robustness. To address this, we propose NeuroShield-ViT—a mechanism-driven, feedforward defense framework. It is the first to systematically characterize the inter-layer propagation and amplification of adversarial effects; identifies early-stage vulnerable neurons via layer-wise sensitivity diagnosis; and applies dynamic, selective neuron shielding—without model fine-tuning or retraining, enabling zero-shot robustification. Under strong iterative attacks, NeuroShield-ViT achieves 77.8% adversarial accuracy, substantially outperforming state-of-the-art robust training and defense methods. It offers high computational efficiency and strong cross-dataset generalization capability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Data transparency and provenance

Abstract

While transformer-based models dominate NLP and vision applications, their underlying mechanisms to map the input space to the label space semantically are not well understood. In this paper, we study the sources of known representation vulnerabilities of vision transformers (ViT), where perceptually identical images can have very different representations and semantically unrelated images can have the same representation. Our analysis indicates that imperceptible changes to the input can result in significant representation changes, particularly in later layers, suggesting potential instabilities in the performance of ViTs. Our comprehensive study reveals that adversarial effects, while subtle in early layers, propagate and amplify through the network, becoming most pronounced in middle to late layers. This insight motivates the development of NeuroShield-ViT, a novel defense mechanism that strategically neutralizes vulnerable neurons in earlier layers to prevent the cascade of adversarial effects. We demonstrate NeuroShield-ViT's effectiveness across various attacks, particularly excelling against strong iterative attacks, and showcase its remarkable zero-shot generalization capabilities. Without fine-tuning, our method achieves a competitive accuracy of 77.8% on adversarial examples, surpassing conventional robustness methods. Our results shed new light on how adversarial effects propagate through ViT layers, while providing a promising approach to enhance the robustness of vision transformers against adversarial attacks. Additionally, they provide a promising approach to enhance the robustness of vision transformers against adversarial attacks.

Problem

• Understanding representation vulnerabilities in vision transformers
• Analyzing adversarial effects propagation in transformer layers
• Developing NeuroShield-ViT for robust defense against attacks

Innovation

• NeuroShield-ViT neutralizes vulnerable neurons
• Analyzes adversarial effects in ViT layers
• Enhances robustness against adversarial attacks