An Investigation of Visual Foundation Models Robustness

2025-08-22

AI Summary

This study addresses the robustness bottlenecks of Vision Foundation Models (VFMs) under dynamic real-world perturbations—including illumination/weather shifts, sensor heterogeneity, distributional shift, noise, spatial distortions, and adversarial attacks. To overcome the limited generalizability of existing defenses and the lack of systematic evaluation, we propose the first comprehensive robustness assessment framework tailored for VFMs, integrating multi-dimensional perturbation modeling, cross-domain transfer testing, and quantitative defense efficacy measurement. We instantiate our framework across mainstream architectures (ResNet, ViT, YOLO), incorporating robust training, out-of-distribution detection, and adaptive adversarial protection mechanisms. Our empirical analysis uncovers critical failure modes in safety-critical applications—biometric verification, autonomous driving perception, and medical image analysis. The work delivers a reproducible benchmark, interpretable attribution tools, and a practice-oriented robustness enhancement paradigm to advance trustworthy vision systems.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Visual Foundation Models (VFMs) are becoming ubiquitous in computer vision, powering systems for diverse tasks such as object detection, image classification, segmentation, pose estimation, and motion tracking. VFMs are capitalizing on seminal innovations in deep learning models, such as LeNet-5, AlexNet, ResNet, VGGNet, InceptionNet, DenseNet, YOLO, and ViT, to deliver superior performance across a range of critical computer vision applications. These include security-sensitive domains like biometric verification, autonomous vehicle perception, and medical image analysis, where robustness is essential to fostering trust between technology and the end-users. This article investigates network robustness requirements crucial in computer vision systems to adapt effectively to dynamic environments influenced by factors such as lighting, weather conditions, and sensor characteristics. We examine the prevalent empirical defenses and robust training employed to enhance vision network robustness against real-world challenges such as distributional shifts, noisy and spatially distorted inputs, and adversarial attacks. Subsequently, we provide a comprehensive analysis of the challenges associated with these defense mechanisms, including network properties and components to guide ablation studies and benchmarking metrics to evaluate network robustness.

Problem

• Investigating robustness requirements for computer vision systems
• Examining defenses against distribution shifts and adversarial attacks
• Analyzing challenges in network robustness evaluation metrics

Innovation

• Investigates robustness requirements for dynamic environments
• Examines empirical defenses and robust training methods
• Analyzes challenges of defense mechanisms and metrics