ADVERSARIAL ROBUSTNESS IN LARGE VISION-LANGUAGE MODELS: DETECTION, DEFENSE, AND CERTIFICATION

Abstract : Adversarial robustness in large vision-language models (VLMs) has emerged as a critical research focus due to the growing deployment of these models in real-world applications, where their vulnerability to adversarial attacks can lead to severe consequences. These attacks exploit subtle, often imperceptible perturbations to input images or text, causing models to produce incorrect or misleading outputs, thereby undermining trust and reliability. This paper presents a comprehensive overview of adversarial robustness in VLMs, focusing on three fundamental aspects: detection, defense, and certification. First, detection methods aim to identify adversarial inputs before they influence the model’s decision-making process. Techniques such as anomaly detection, input reconstruction, and model uncertainty estimation are discussed, highlighting their effectiveness and limitations in the vision-language domain. Next, defense strategies are explored, including adversarial training, input preprocessing, and robust architecture design, which seek to enhance the model’s resilience against adversarial manipulations. We examine how these defenses can be tailored to the multi-modal nature of VLMs, addressing unique challenges such as the alignment of visual and textual modalities under attack. Additionally, we analyze emerging defense paradigms leveraging self-supervised learning and contrastive objectives that promote intrinsic robustness. Finally, certification approaches are reviewed, which provide theoretical guarantees on the robustness of VLMs within certain perturbation bounds, thereby offering provable assurance against adversarial examples. We discuss advances in randomized smoothing and verification techniques adapted for multi-modal inputs, emphasizing their role in establishing formal robustness benchmarks. Throughout the paper, we underscore the interplay between detection, defense, and certification, advocating for integrated frameworks that jointly address these facets to build more secure and reliable VLMs. We also identify key challenges and future directions, such as scalability to large-scale models, robustness to diverse and adaptive attack vectors, and the need for standardized evaluation protocols specific to vision-language tasks. By synthesizing recent developments and providing a holistic perspective, this work aims to guide researchers and practitioners in advancing adversarial robustness for large vision-language models, ultimately facilitating safer deployment in sensitive domains including autonomous systems, healthcare, and content moderation.

Keyword adversarial robustness, vision-language models, adversarial detection, defense strategies, robustness certification, multi-modal learning