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20242026
most citedOver-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis

1 citations · 1 across the 3 of their papers we have counts for

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cs.CR2026

Architectural Backdoors in Vision-Language Model Supply Chains via Representation Steering

Maria Rosaria Briglia, Igor Maljkovic, Antonio Emanuele Cinà +3

Vision--Language Models (VLMs) are increasingly deployed through a model supply chain in which pretrained checkpoints, architecture definitions, text encoders, and exported computa…

cs.CR2026

Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization

Igor Maljkovic, Maria Rosaria Briglia, Iacopo Masi +2

Vision-Language Models (VLMs) have become essential for tasks such as image synthesis, captioning, and retrieval by aligning textual and visual information in a shared embedding sp…

cs.CR2025

Evaluating the Evaluators: Trust in Adversarial Robustness Tests

Antonio Emanuele CinÃ, Maura Pintor, Luca Demetrio +3

Despite significant progress in designing powerful adversarial evasion attacks for robustness verification, the evaluation of these methods often remains inconsistent and unreliabl…

cs.CR2025

Energy-Latency Attacks via Sponge Poisoning

Antonio Emanuele CinÃ, Ambra Demontis, Battista Biggio +2

Sponge examples are test-time inputs optimized to increase energy consumption and prediction latency of deep networks deployed on hardware accelerators. By increasing the fraction…

cs.CR2024

Pirates of Charity: Exploring Donation-based Abuses in Social Media Platforms

Bhupendra Acharya, Dario Lazzaro, Antonio Emanuele Cinà +1

With the widespread use of social media, organizations, and individuals use these platforms to raise funds and support causes. Unfortunately, this has led to the rise of scammers i…

cs.CR2024

Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms

Francesco Villani, Dario Lazzaro, Antonio Emanuele Cinà +3

Data poisoning attacks on clustering algorithms have received limited attention, with existing methods struggling to scale efficiently as dataset sizes and feature counts increase.…