activity
20212026
most citedRevisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

2 citations · 3 across the 13 of their papers we have counts for

collaborators
Showing 2026Show all

9 papers · 1 filter

math.FA2026

Embeddings of Generalized Weighted Morrey Spaces

Zhen Liu

We study continuous embeddings from two classes of generalized weighted Morrey spaces into generalized Morrey spaces. The Komori--Shirai-type spaces use the weighted measure of cub…

cs.CV2026

Certification of Real Images through Calibrated Content Authentication

Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam +2

Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detectors against ten generators re…

cs.CV2026

ShieldCLIP: Selective Safety Alignment for Harmful Content Mitigation in Multimodal Foundation Models

Tobia Poppi, Silvia Cappelletti, Samuele Poppi +4

Multimodal encoders such as CLIP underlie many downstream systems, but their web-scale training data embed harmful associations that safety alignment must suppress without unnecess…

cs.LG2026

CopyShield: A Cross-Level Benchmark of Copyright Defenses in LLMs

Maryam Alshehyari, Dushyant Singh Chauhan, Samuele Poppi +3

Large language models can reproduce memorized text verbatim, yet copyright defenses are usually evaluated under incompatible protocols. We introduce CopyShield, a controlled benchm…

cs.CR2026

Context Inference Attacks Without Jailbreaks

Prince Jha, Samuele Poppi, Nils Lukas

Agentic AI systems are increasingly deployed to process sensitive data at inference time, such as healthcare records or financial documents assembled into a hidden \emph{context} b…

cs.CV2026

ReACT-CLIP: Response-Aware Test-Time Defense for Vision--Language Models

Hashmat Shadab Malik, Toluwani Aremu, Samuele Poppi +2

Training-free test-time defenses offer a practical way to improve the adversarial robustness of CLIP-style vision--language models without modifying the pretrained model. However,…

cs.LG2026

A Gravitational Interpretation of Safety Reversion under Fine-Tuning

Samuele Poppi, Nils Lukas

Safety alignment in large language models can degrade during post-training even when neither the data nor the objective is intentionally adversarial. Alignment rebound and reverse…

cs.AI2026

When the Chain of Thought Knows Better: Failure Modes in Multi-Turn Reasoning Models

Sai Kartheek Reddy Kasu, Nils Lukas, Samuele Poppi

Failures in multi-turn reasoning models are largely invisible to terminal-score evaluation. A model can lock onto an unsafe stance early in a long dialogue, yet its final-turn refu…

cs.CR2026

Adaptively Robust LLM Monitoring via Activation Watermarking

Toluwani Aremu, Daniil Ognev, Samuele Poppi +1

Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent. LLM monitoring is deterministic and often openly available, so …