Right Predictions, Misleading Explanations: On the Vulnerability of Vision-Language Model Explanations
arXiv:2605.16651v2 Announce Type: replace-cross Abstract: Explanation mechanisms are increasingly used to support transparency and trust in vision-language models (VLMs), particularly in settings where model decisions require human oversight. However, the robustness of these explanations remains insufficiently understood. In this work, we investigate whether explanation heatmaps in VLMs, particularly CLIP-based models, faithfully reflect model reasoning under adversarial conditions. We show that explanation maps can be systematically manipulated while preserving the model's original prediction
As AI models, particularly vision-language models, become more integrated into critical decision-making processes, the need for reliable transparency and interpretability is acutely felt.
The demonstrated vulnerability of VLM explanations to adversarial manipulation undermines trust in AI systems and poses significant risks for applications requiring accountability and human oversight.
The understanding of AI interpretability shifts from merely providing explanations to critically evaluating the robustness and trustworthiness of those explanations, especially under adversarial conditions.
- · AI robustness and interpretability researchers
- · Developers of secure AI systems
- · Auditors of AI deployments
- · Developers of vulnerable explanation methods
- · Users relying uncritically on VLM explanations
- · Sectors with high-stakes VLM applications
This discovery will drive immediate research into more robust and verifiable explanation techniques for vision-language models.
Increased regulatory scrutiny on explanation integrity in AI systems will follow, particularly for high-risk applications.
A new industry for 'adversarial explanation testing' and 'explanation auditing' might emerge to ensure AI transparency is not just superficial.
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Read at arXiv cs.LG