3 citations · 6 across the 9 of their papers we have counts for
10 papers
Compositional Adversarial Training for Robust Visual Watermarking
Anirudh Satheesh, Michael-Andrei Panaitescu-Liess, Andrew Xu +4
Robust watermarking is typically trained with random post-processing augmentation, but random sampling under-covers the combinatorial space of realistic attack pipelines and rarely…
RAGPart & RAGMask: Retrieval-Stage Defenses Against Corpus Poisoning in Retrieval-Augmented Generation
Pankayaraj Pathmanathan, Michael-Andrei Panaitescu-Liess, Cho-Yu Jason Chiang +1
Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to enhance large language models (LLMs) with external knowledge, reducing hallucinations and compensating f…
Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds
Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, Sicheng Zhu +2
Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group…
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models
Michael-Andrei Panaitescu-Liess, Pankayaraj Pathmanathan, Yigitcan Kaya +5
As the capabilities of large language models (LLMs) continue to expand, their usage has become increasingly prevalent. However, as reflected in numerous ongoing lawsuits regarding…
AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignment
Pankayaraj Pathmanathan, Udari Madhushani Sehwag, Michael-Andrei Panaitescu-Liess +1
With the growing adoption of reinforcement learning with human feedback (RLHF) for aligning large language models (LLMs), the risk of backdoor installation during alignment has inc…
Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
Bang An, Sicheng Zhu, Ruiyi Zhang +3
Safety-aligned large language models (LLMs) sometimes falsely refuse pseudo-harmful prompts, like "how to kill a mosquito," which are actually harmless. Frequent false refusals not…
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
Michael-Andrei Panaitescu-Liess, Zora Che, Bang An +6
Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as…
PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
Bang An, Sicheng Zhu, Michael-Andrei Panaitescu-Liess +2
Vision-language models like CLIP are widely used in zero-shot image classification due to their ability to understand various visual concepts and natural language descriptions. How…
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes
Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yiğitcan Kaya +1
Quantization is a popular technique that the parameter representation of a neural network from floating-point numbers into lower-precision ones (, 8-bit integers…
Self-Supervised Representation Learning on Document Images
Adrian Cosma, Mihai Ghidoveanu, Michael Panaitescu-Liess +1
This work analyses the impact of self-supervised pre-training on document images in the context of document image classification. While previous approaches explore the effect of se…