21 citations · 54 across the 9 of their papers we have counts for
11 papers
Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem
Amit Peleg, Naman Deep Singh, Naama Pearl +2
Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety. Yet existing benchmarks measure i…
Unlearning That Lasts: Utility-Preserving, Robust, and Almost Irreversible Forgetting in LLMs
Naman Deep Singh, Maximilian Müller, Francesco Croce +1
Unlearning in large language models (LLMs) involves precisely removing specific information from a pre-trained model. This is crucial to ensure safety of LLMs by deleting private d…
Robustness in Both Domains: CLIP Needs a Robust Text Encoder
Elias Abad Rocamora, Christian Schlarmann, Naman Deep Singh +3
Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-…
Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
Amit Peleg, Naman Deep Singh, Matthias Hein
Vision-language models like CLIP have demonstrated remarkable zero-shot capabilities in classification and retrieval. However, these models often struggle with compositional reason…
Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics
Francesco Croce, Christian Schlarmann, Naman Deep Singh +1
Measuring perceptual similarity is a key tool in computer vision. In recent years perceptual metrics based on features extracted from neural networks with large and diverse trainin…
Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP
Naman Deep Singh, Francesco Croce, Matthias Hein
Vision-Language models like CLIP have been shown to be highly effective at linking visual perception and natural language understanding, enabling sophisticated image-text capabilit…
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
Christian Schlarmann, Naman Deep Singh, Francesco Croce +1
Multi-modal foundation models like OpenFlamingo, LLaVA, and GPT-4 are increasingly used for various real-world tasks. Prior work has shown that these models are highly vulnerable t…
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models
Francesco Croce, Naman D Singh, Matthias Hein
Adversarial robustness has been studied extensively in image classification, especially for the -threat model, but significantly less so for related tasks such as obje…
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
Naman D Singh, Francesco Croce, Matthias Hein
While adversarial training has been extensively studied for ResNet architectures and low resolution datasets like CIFAR, much less is known for ImageNet. Given the recent debate ab…
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
Francesco Croce, Maksym Andriushchenko, Naman D. Singh +2
We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on subs…
Clustering and Learning from Imbalanced Data
Naman D. Singh, Abhinav Dhall
A learning classifier must outperform a trivial solution, in case of imbalanced data, this condition usually does not hold true. To overcome this problem, we propose a novel data l…