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[2308. 11804] Adversarial Illusions in Multi-Modal Embeddings In this paper, we show that multi-modal embeddings can be vulnerable to an attack we call "adversarial illusions " Given an image or a sound, an adversary can perturb it to make its embedding close to an arbitrary, adversary-chosen input in another modality
Adversarial Illusions in Multi-Modal Embeddings - USENIX In this paper, we show that multi-modal embeddings can be vulnerable to an attack we call "adversarial illusions " Given an image or a sound, an adversary can perturb it to make its embedding close to an arbitrary, adversary-chosen input in another modality
Adversarial Illusions in Multi-Modal Embeddings - GitHub In this paper, we show that multi-modal embeddings can be vulnerable to an attack we call "adversarial illusions " Given an image or a sound, an adversary can perturb it to make its embedding close to an arbitrary, adversary-chosen input in another modality
Rishi Jha In this paper, we show that multi-modal embeddings can be vulnerable to an attack we call "adversarial illusions " Given an image or a sound, an adversary can perturb it to make its embedding close to an arbitrary, adversary-chosen input in another modality
USENIX Security ’24 Artifact Appendix: Adversarial Illusions in Multi . . . The artifact demonstrates the vulner-ability of multi-modal embeddings to adversarial illusions: given an image or a sound, an adversary can perturb it to make its embedding close to an arbitrary, adversary-chosen input in another modality
RSAC™ 2025 Conference Quick Look: Adversarial Illusions in Multi-Modal . . . In this presentation, we show that multi-modal embeddings can be vulnerable to an attack we call "adversarial illusions " Given an image or a sound, an adversary can perturb it to make its embedding close to an arbitrary, adversary-chosen input in another modality See the full session here
[2511. 21893] Breaking the Illusion: Consensus-Based Generative . . . Multi-modal foundation models align images, text, and other modalities in a shared embedding space but remain vulnerable to adversarial illusions (Zhang et al , 2025), where imperceptible perturbations disrupt cross-modal alignment and mislead downstream tasks To counteract the effects of adversarial illusions, we propose a task-agnostic mitigation mechanism that reconstructs the input from
Adversarial Illusions in Multi-Modal Embeddings - USENIX In this paper, we show that multi-modal embeddings can be vulnerable to an attack we call “adversarial illusions ” Given an image or a sound, an adversary can perturb it to make its embedding close to an arbitrary, adversary-chosen input in another modality
Multimodal learning - Wikipedia Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and