Paper-Conference

Correlating Adversarial Robustness with the Energy of Untrained Classifiers
Neural networks are known to be vulnerable to imperceptible perturbations to their inputs, causing unwarranted behavior in the …
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Correlating Adversarial Robustness with the Energy of Untrained Classifiers
Neutralizing Proactive Defense using Diffusion-based Upsampling
The rapid spread of open-source generative models has made it easy to create highly realistic manipulated media, posing a critical …
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Neutralizing Proactive Defense using Diffusion-based Upsampling
A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models
Despite the rapid progress in multimodal models and Large Visual-Language Models (LVLM), they remain highly susceptible to adversarial …
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A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models
Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization
Vision–Language Models (VLMs) have become essential for tasks such as image synthesis, captioning, and retrieval by aligning textual …
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Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization
Implicit Inversion turns CLIP into a Decoder
CLIP is a discriminative model trained to align images and text in a shared embedding space. Due to its multimodal structure, it serves …
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Implicit Inversion turns CLIP into a Decoder
Spilled Energy in Large Language Models
We reinterpret the final softmax classifier over the vocabulary of Large Language Models (LLM) as an Energy-based Model (EBM). This …
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Spilled Energy in Large Language Models
Why Adversarially Train Diffusion Models?
We answer the question in the title showing that adversarial training (AT) for diffusion models (DMs) fundamentally differs from …
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Why Adversarially Train Diffusion Models?
MASS : MoErging through Adaptive Subspace Selection
Model merging has recently emerged as a lightweight alternative to ensembling, combining multiple fine-tuned models into a single set …
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MASS : MoErging through Adaptive Subspace Selection
Inverse Language Modeling for Robust and Grounded LLMs
The current landscape of defensive mechanisms for LLMs is fragmented and underdeveloped, unlike prior work on classifiers. To further …
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Inverse Language Modeling for Robust and Grounded LLMs
Genμ: The Generative Machine Unlearning Challenge
Generative machine unlearning has emerged as a critical requirement for the responsible deployment of text-to-image generative models, …
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Genμ: The Generative Machine Unlearning Challenge