arXiv:2512.12997v2 Announce Type: replace-cross Abstract: CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks. Prior adversarial fine-tuning work primarily matches predicted logits between clean and adversarial examples, which overlooks uncertainty calibration and may degrade the zero-shot generalization. A common expectation in reliable uncertainty estimation is that predictive uncertainty should increase as inputs become more difficult or shift away from the training distribution. However, we frequently observe the opposite in the adversarial se
The paper addresses a critical gap in robust AI development, focusing on uncertainty calibration in adversarial settings for large models like CLIP, which is increasingly relevant as AI deployment expands in sensitive domains.
Improving the trustworthiness and reliability of AI systems, especially against adversarial attacks, is paramount for their widespread and safe adoption across industries, influencing regulatory frameworks and public trust.
This research suggests a shift towards more robust and uncertainty-aware AI models, potentially leading to more secure and generalizable zero-shot classification capabilities, reducing vulnerabilities in real-world applications.
- · AI developers focused on security
- · Industries deploying AI in sensitive areas (e.g., defense, finance)
- · Researchers in adversarial AI
- · Adversarial attackers
- · Organizations relying on uncalibrated AI models
AI models become more resilient to adversarial attacks and provide more reliable uncertainty estimates.
Increased confidence in AI deployments leads to faster integration of AI into critical infrastructure and decision-making processes.
The development of 'red teaming' for AI shifts to focus on more sophisticated, uncertainty-aware vulnerabilities.
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Read at arXiv cs.LG