5 citations · 17 across the 12 of their papers we have counts for
13 papers
SpecHop: Continuous Speculation for Accelerating Multi-Hop Retrieval Agents
Mehrdad Saberi, Keivan Rezaei, Soheil Feizi
Large language models increasingly use external tools such as web search and document retrieval to solve information-intensive tasks. However, multi-hop tool use in complex tasks i…
IConMark: Robust Interpretable Concept-Based Watermark For AI Images
Vinu Sankar Sadasivan, Mehrdad Saberi, Soheil Feizi
With the rapid rise of generative AI and synthetic media, distinguishing AI-generated images from real ones has become crucial in safeguarding against misinformation and ensuring d…
Revisiting the Past: Data Unlearning with Model State History
Keivan Rezaei, Mehrdad Saberi, Abhilasha Ravichander +1
Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, or data that degrades model perfo…
Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text
Yize Cheng, Vinu Sankar Sadasivan, Mehrdad Saberi +2
The increasing capabilities of Large Language Models (LLMs) have raised concerns about their misuse in AI-generated plagiarism and social engineering. While various AI-generated te…
Securing the Future of GenAI: Policy and Technology
Mihai Christodorescu, Ryan Craven, Soheil Feizi +11
The rise of Generative AI (GenAI) brings about transformative potential across sectors, but its dual-use nature also amplifies risks. Governments globally are grappling with the ch…
DREW : Towards Robust Data Provenance by Leveraging Error-Controlled Watermarking
Mehrdad Saberi, Vinu Sankar Sadasivan, Arman Zarei +2
Identifying the origin of data is crucial for data provenance, with applications including data ownership protection, media forensics, and detecting AI-generated content. A standar…
Improving Compositional Attribute Binding in Text-to-Image Generative Models via Enhanced Text Embeddings
Arman Zarei, Keivan Rezaei, Samyadeep Basu +4
Text-to-image diffusion-based generative models have the stunning ability to generate photo-realistic images and achieve state-of-the-art low FID scores on challenging image genera…
EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods
Samyadeep Basu, Mehrdad Saberi, Shweta Bhardwaj +5
A plethora of text-guided image editing methods have recently been developed by leveraging the impressive capabilities of large-scale diffusion-based generative models such as Imag…
PRIME: Prioritizing Interpretability in Failure Mode Extraction
Keivan Rezaei, Mehrdad Saberi, Mazda Moayeri +1
In this work, we study the challenge of providing human-understandable descriptions for failure modes in trained image classification models. Existing works address this problem by…
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
Mehrdad Saberi, Vinu Sankar Sadasivan, Keivan Rezaei +4
In light of recent advancements in generative AI models, it has become essential to distinguish genuine content from AI-generated one to prevent the malicious usage of fake materia…
Robust Routing Made Easy: Reinforcing Networks Against Non-Benign Faults
Christoph Lenzen, Moti Medina, Mehrdad Saberi +1
With the increasing scale of communication networks, the likelihood of failures grows as well. Since these networks form a critical backbone of our digital society, it is important…
ZeroGrad : Mitigating and Explaining Catastrophic Overfitting in FGSM Adversarial Training
Zeinab Golgooni, Mehrdad Saberi, Masih Eskandar +1
Making deep neural networks robust to small adversarial noises has recently been sought in many applications. Adversarial training through iterative projected gradient descent (PGD…
Robust Routing Made Easy
Christoph Lenzen, Moti Medina, Mehrdad Saberi +1
With the increasing scale of communication networks, the likelihood of failures grows as well. Since these networks form a critical backbone of our digital society, it is important…