Priyatham Kattakinda — accepted AI-conference papers

2025

Rethinking Artistic Copyright Infringements In the Era Of Text-to-Image Generative Models

Mazda Moayeri, Sriram Balasubramanian, Samyadeep Basu, Priyatham Kattakinda, Atoosa Chegini, Robert Brauneis +1

ICLR 2025poster

The advent of text-to-image generative models has led artists to worry that their individual styles may be copied, creating a pressing need to reconsider the lack of protection for artistic styles under copyright law. This requires answering challenging questions, like what defines style and what co…

Cited by 4 Source ↗PDF ↗Scholar ↗

2024

Fast Adversarial Attacks on Language Models In One GPU Minute

Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda, Atoosa Chegini, Soheil Feizi

ICML 2024poster

In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, success rate, and the readability of adversarial prompts. The computational efficienc…

Cited by 27 Source ↗PDF ↗code ↗Scholar ↗

2024

LLM-Check: Investigating Detection of Hallucinations in Large Language Models

Gaurang Sriramanan, Siddhant Bharti, Vinu Sankar Sadasivan, Shoumik Saha, Priyatham Kattakinda, Soheil Feizi

NeurIPS 2024poster

While Large Language Models (LLMs) have become immensely popular due to their outstanding performance on a broad range of tasks, these models are prone to producing hallucinations— outputs that are fallacious or fabricated yet often appear plausible or tenable at a glance. In this paper, we conduct…

Cited by 143 Source ↗PDF ↗Scholar ↗

2024

On Mechanistic Knowledge Localization in Text-to-Image Generative Models

Samyadeep Basu, Keivan Rezaei, Priyatham Kattakinda, Vlad I Morariu, Nanxuan Zhao, Ryan A. Rossi +2

ICML 2024poster

Identifying layers within text-to-image models which control visual attributes can facilitate efficient model editing through closed-form updates. Recent work, leveraging causal tracing show that early Stable-Diffusion variants confine knowledge primarily to the first layer of the CLIP text-encoder,…

Cited by 14 Source ↗PDF ↗code ↗Scholar ↗

2022

FOCUS: Familiar Objects in Common and Uncommon Settings

Priyatham Kattakinda, Soheil Feizi

ICML 2022spotlight

Standard training datasets for deep learning often do not contain objects in uncommon and rare settings (e.g., “a plane on water”, “a car in snowy weather”). This can cause models trained on these datasets to incorrectly predict objects that are typical for the context in the image, rather than iden…

Cited by 12 Source ↗PDF ↗code ↗Scholar ↗