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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.CV2026

Representation and Reference Selection in Training-Free Synthetic Image Attribution

Meiling Li, Pietro Bongini, Benedetta Tondi +1

The paper investigates how the choice of visual representation and reference selection method affect training‑free, reference‑based attribution of synthetic images, showing that in…

stat.ML2026

The Role of Input Dimensionality in the Emergence and Targeted Control of Adversarial Examples

Nasrin Malekzadeh Goradel, Niccolo Pancino, Yaser Gholizade Atani +3

Several theoretical works have tried to explain the adversarial vulnerability of deep neural networks through properties of high-dimensional geometry. However, the assumptions unde…

cs.CV2026

Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models

Jianwei Fei, Yunshu Dai, Zhihua Xia +4

Model fingerprinting, embedding user-specific identifiers (fingerprints) into generated outputs, has recently emerged as a popular solution to protect the intellectual property rig…

cs.CV2025

Enhancing Visual Sentiment Analysis via Semiotic Isotopy-Guided Dataset Construction

Marco Blanchini, Giovanna Maria Dimitri, Benedetta Tondi +2

Visual Sentiment Analysis (VSA) is a challenging task due to the vast diversity of emotionally salient images and the inherent difficulty of acquiring sufficient data to capture th…

cs.CV2025

An Efficient Watermarking Method for Latent Diffusion Models via Low-Rank Adaptation and Dynamic Loss Weighting

Dongdong Lin, Yue Li, Benedetta Tondi +3

The rapid proliferation of Deep Neural Networks (DNNs) is driving a surge in model watermarking technologies, as the trained models themselves constitute valuable intellectual prop…

cs.CV2025

Training-free Source Attribution of AI-generated Images via Resynthesis

Pietro Bongini, Valentina Molinari, Andrea Costanzo +2

Synthetic image source attribution is a challenging task, especially in data scarcity conditions requiring few-shot or zero-shot classification capabilities. We present a new train…