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- Anupam ChattopadhyaySuggested from co-authorship
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Works9 from public data
- Persistence of Backdoor-Based Watermarks for Neural Networks: A Comprehensive Evaluation7
Empirical results show that by solely introducing training data after fine-tuning, the watermark can be restored if model parameters do not shift dramatically during fine-tuning, and depending on the types of trigger samples used, trigger accuracy can be reinstated to up to 100%.
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- BlockDoor: Blocking Backdoor Based Watermarks in Deep Neural Networks2
This paper presents BlockDoor, which is a comprehensive package of techniques that is used as a wrapper to block all three different kinds of Trigger samples, which are used in the literature as means to embed watermarks within the trained neural networks as backdoors.
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- NutVLM: A Self-Adaptive Defense Framework against Full-Dimension Attacks for Vision Language Models in Autonomous Driving1
The proposed NutVLM is a comprehensive self-adaptive defense framework designed to secure the entire perception-decision lifecycle of Vision Language Models, and its results validate NutVLM as a scalable security solution for intelligent transportation.
- Vaporizer: Breaking Watermarking Schemes for Large Language Model Outputs–
Light is shed on the strengths and weaknesses of existing LLM watermarking systems, suggesting how they should be constructed to improve security of available schemes.
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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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