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- Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers13
This paper proposes a novel method, named Adversarial Watermark Defender with Attribution-Guided Perturbation (AWD-AGP), that defends against both inpainting-based and blind watermark removers under a black-box setting, and is the first watermark protection method employing adversarial location.
- Leveraging Imperfect Restoration for Data Availability Attack3
A novel poisoning method named Imperfect Restoration Poisoning (IRP), aiming to preserve high image quality while achieving strong poisoning effects, is proposed, aiming to preserve high image quality while achieving strong poisoning effects.
- Transferable Attack against Face Swapping in an Extended Space1
Extensive experiments using 1000 image pairs across various state-of-the-art subject-agnostic FS models, including GAN and diffusion-based FS models, show that AIR surpasses all existing attacks in terms of both attack success rate and image quality.
- Exploiting Supervised Poison Vulnerability to Strengthen Self-supervised Defense1
This work introduces adversarial training (AT) on SL to obfuscate poison features and guide robust feature learning for self-supervised learning for SSL, and elucidate the mechanisms by which VESPR learns robust class features.
- New Threats Against Object Detector with Non-local Block1
Two new threats named disappearing attack and appearing attack against object detectors with a non-local block are investigated, which aim at misleading an object detector with a non-local block such that it is unable to detect a target object category.
- Non-local MMDenseNet with Cross-Band Features for Audio Source Separation1
This work proposes a novel Non-Local Multi-scale Multi-band DenseNet model termed as NLMMDenseNet for audio source separation by jointly exploring the long-term dependencies and recovering the missing information around bands’ borders.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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