Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909352962555904 |
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| author | Daw, Arka Chung, Megan Hong-Thanh Mahbub, Maria Sadovnik, Amir |
| author_facet | Daw, Arka Chung, Megan Hong-Thanh Mahbub, Maria Sadovnik, Amir |
| contents | Machine learning models are known to be vulnerable to adversarial attacks, but traditional attacks have mostly focused on single-modalities. With the rise of large multi-modal models (LMMs) like CLIP, which combine vision and language capabilities, new vulnerabilities have emerged. However, prior work in multimodal targeted attacks aim to completely change the model's output to what the adversary wants. In many realistic scenarios, an adversary might seek to make only subtle modifications to the output, so that the changes go unnoticed by downstream models or even by humans. We introduce Hiding-in-Plain-Sight (HiPS) attacks, a novel class of adversarial attacks that subtly modifies model predictions by selectively concealing target object(s), as if the target object was absent from the scene. We propose two HiPS attack variants, HiPS-cls and HiPS-cap, and demonstrate their effectiveness in transferring to downstream image captioning models, such as CLIP-Cap, for targeted object removal from image captions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13010 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images Daw, Arka Chung, Megan Hong-Thanh Mahbub, Maria Sadovnik, Amir Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition Machine learning models are known to be vulnerable to adversarial attacks, but traditional attacks have mostly focused on single-modalities. With the rise of large multi-modal models (LMMs) like CLIP, which combine vision and language capabilities, new vulnerabilities have emerged. However, prior work in multimodal targeted attacks aim to completely change the model's output to what the adversary wants. In many realistic scenarios, an adversary might seek to make only subtle modifications to the output, so that the changes go unnoticed by downstream models or even by humans. We introduce Hiding-in-Plain-Sight (HiPS) attacks, a novel class of adversarial attacks that subtly modifies model predictions by selectively concealing target object(s), as if the target object was absent from the scene. We propose two HiPS attack variants, HiPS-cls and HiPS-cap, and demonstrate their effectiveness in transferring to downstream image captioning models, such as CLIP-Cap, for targeted object removal from image captions. |
| title | Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.13010 |