A Generative Adversarial Approach to Adversarial Attacks Guided by Contrastive Language-Image Pre-trained Model

• URL: http://arxiv.org/abs/2511.01317v1
• Date: Mon, 03 Nov 2025 08:02:48 GMT
• Title: A Generative Adversarial Approach to Adversarial Attacks Guided by Contrastive Language-Image Pre-trained Model
• Authors: Sampriti Soor, Alik Pramanick, Jothiprakash K, Arijit Sur,
• Abstract summary: A generative adversarial attack method is proposed that uses the CLIP model to create highly effective and visually imperceptible adversarial perturbations.<n>Our approach integrates the concentrated perturbation strategy from Saliency-based Auto-Encoder with the dissimilar text embeddings similar to Generative Adversarial Multi-Object Scene Attacks (GAMA)
• Score: 12.15621649989295
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid growth of deep learning has brought about powerful models that can handle various tasks, like identifying images and understanding language. However, adversarial attacks, an unnoticed alteration, can deceive models, leading to inaccurate predictions. In this paper, a generative adversarial attack method is proposed that uses the CLIP model to create highly effective and visually imperceptible adversarial perturbations. The CLIP model's ability to align text and image representation helps incorporate natural language semantics with a guided loss to generate effective adversarial examples that look identical to the original inputs. This integration allows extensive scene manipulation, creating perturbations in multi-object environments specifically designed to deceive multilabel classifiers. Our approach integrates the concentrated perturbation strategy from Saliency-based Auto-Encoder (SSAE) with the dissimilar text embeddings similar to Generative Adversarial Multi-Object Scene Attacks (GAMA), resulting in perturbations that both deceive classification models and maintain high structural similarity to the original images. The model was tested on various tasks across diverse black-box victim models. The experimental results show that our method performs competitively, achieving comparable or superior results to existing techniques, while preserving greater visual fidelity.

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