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20132026
most citedAligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

227 citations · 247 across the 25 of their papers we have counts for

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14 papers · 1 filter

cs.CR2025

Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size

Benedetta Tondi, Andrea Costanzo, Mauro Barni

We propose a high-payload image watermarking method for textual embedding, where a semantic description of the image - which may also correspond to the input text prompt-, is embed…

cs.CR2022

CycleGANWM: A CycleGAN watermarking method for ownership verification

Dongdong Lin, Benedetta Tondi, Bin Li +1

Due to the proliferation and widespread use of deep neural networks (DNN), their Intellectual Property Rights (IPR) protection has become increasingly important. This paper present…

cs.CR2020★ 4 cited

Spread-Transform Dither Modulation Watermarking of Deep Neural Network

Yue Li, Benedetta Tondi, Mauro Barni

DNN watermarking is receiving an increasing attention as a suitable mean to protect the Intellectual Property Rights associated to DNN models. Several methods proposed so far are i…

cs.CR2020

Challenging the adversarial robustness of DNNs based on error-correcting output codes

Bowen Zhang, Benedetta Tondi, Xixiang Lv +1

The existence of adversarial examples and the easiness with which they can be generated raise several security concerns with regard to deep learning systems, pushing researchers to…

cs.CR2019

Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples

Mauro Barni, Ehsan Nowroozi, Benedetta Tondi +1

We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on d…

cs.CR2019★ 4 cited

Attacking CNN-based anti-spoofing face authentication in the physical domain

Bowen Zhang, Benedetta Tondi, Mauro Barni

In this paper, we study the vulnerability of anti-spoofing methods based on deep learning against adversarial perturbations. We first show that attacking a CNN-based anti-spoofing…