On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations

Jordan Vice; Naveed Akhtar; Yansong Gao; Richard Hartley; Ajmal Mian

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arXiv · 2507.22398

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations

Jordan Vice·Naveed Akhtar·Yansong Gao·Richard Hartley·Ajmal Mian

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Abstract

Vision-Language Models (VLMs) are increasingly used as perceptual modules for visual content reasoning, including through captioning and DeepFake detection. In this work, we expose a critical vulnerability of VLMs when exposed to subtle, structured perturbations in the frequency domain. Specifically, we highlight how these feature transformations undermine authenticity/DeepFake detection and automated image captioning tasks. We design targeted image transformations, operating in the frequency domain to systematically adjust VLM outputs when exposed to frequency-perturbed real and synthetic images. We demonstrate that the perturbation injection method generalizes across five state-of-the-art VLMs which includes different-parameter Qwen2/2.5 and BLIP models. Experimenting across ten real and generated image datasets reveals that VLM judgments are sensitive to frequency-based cues and may not wholly align with semantic content. Crucially, we show that visually-imperceptible spatial frequency transformations expose the fragility of VLMs deployed for automated image captioning and authenticity detection tasks. Our findings under realistic, black-box constraints challenge the reliability of VLMs, underscoring the need for robust multimodal perception systems.

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Jordan Vice, Naveed Akhtar, Yansong Gao, Richard Hartley, Ajmal Mian. 2025-08-13. On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations. https://arxiv.org/abs/2507.22398

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