{"total":19,"rows":[{"id":"2404.10335","title":"Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models","created":"2024-04-16","cited_by_count":38,"area":"mllm"},{"id":"2412.00114","title":"SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments","created":"2024-11-28","cited_by_count":22,"area":"mllm"},{"id":"2505.05528","title":"X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP","created":"2025-05-08","cited_by_count":21,"area":"mllm"},{"id":"2411.02669","title":"Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack","created":"2024-11-04","cited_by_count":18,"area":"mllm"},{"id":"2505.17440","title":"VEAttack: Downstream-agnostic Vision Encoder Attack against Large Vision Language Models","created":"2025-05-23","cited_by_count":8,"area":"mllm"},{"id":"2503.12874","title":"Evolution-based Region Adversarial Prompt Learning for Robustness Enhancement in Vision-Language Models","created":"2025-03-17","cited_by_count":6,"area":"mllm"},{"id":"2410.11639","title":"Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models","created":"2024-10-15","cited_by_count":4,"area":"mllm"},{"id":"2502.08079","title":"MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models","created":"2025-02-12","cited_by_count":2,"area":"mllm"},{"id":"2601.19210","title":"Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP","created":"2026-01-27","cited_by_count":2,"area":"mllm"},{"id":"2605.21541","title":"Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs","created":"2026-05-20","cited_by_count":2,"area":"mllm"},{"id":"2505.24227","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","created":"2025-05-30","cited_by_count":1,"area":"mllm"},{"id":"2409.06726","title":"Feedback-based Modal Mutual Search for Attacking Vision-Language Pre-training Models","created":"2024-08-27","cited_by_count":0,"area":"mllm"},{"id":"2601.12304","title":"A Two-Stage Globally-Diverse Adversarial Attack for Vision-Language Pre-training Models","created":"2026-01-18","cited_by_count":0,"area":"mllm"},{"id":"2603.04839","title":"Towards Highly Transferable Vision-Language Attack via Semantic-Augmented Dynamic Contrastive Interaction","created":"2026-03-05","cited_by_count":0,"area":"mllm"},{"id":"2603.12799","title":"What Makes VLMs Robust? Towards Reconciling Robustness and Accuracy in Vision-Language Models","created":"2026-03-13","cited_by_count":0,"area":"mllm"},{"id":"2604.16499","title":"HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models","created":"2026-04-14","cited_by_count":0,"area":"mllm"},{"id":"2606.10571","title":"Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction","created":"2026-06-09","cited_by_count":0,"area":"mllm"},{"id":"2607.14974","title":"On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline","created":"2026-07-16","cited_by_count":0,"area":"mllm"},{"id":"2610.06977","title":"Visual-Invariance-Augmented Feature Optimal Alignment for Transferable Adversarial Attacks against Closed-Source MLLMs","created":"2026-10-04","cited_by_count":0,"area":"mllm"}],"next_cursor":null}