2 citations · 2 across the 11 of their papers we have counts for
12 papers
A Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks
Kassem Kallas
Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing hav…
TriniMark: A Robust Generative Speech Watermarking Method for Trinity-Level Traceability
Yue Li, Weizhi Liu, Kaiqing Lin +2
Diffusion-based speech generation has achieved remarkable fidelity, increasing the risk of misuse and unauthorized redistribution. However, most existing generative speech watermar…
Energy Backdoor Attack to Deep Neural Networks
Hanene F. Z. Brachemi Meftah, Wassim Hamidouche, Sid Ahmed Fezza +2
The rise of deep learning (DL) has increased computing complexity and energy use, prompting the adoption of application specific integrated circuits (ASICs) for energy-efficient ed…
Deep Learning for Resilient Adversarial Decision Fusion in Byzantine Networks
Kassem Kallas
This paper introduces a deep learning-based framework for resilient decision fusion in adversarial multi-sensor networks, providing a unified mathematical setup that encompasses di…
Mixer: DNN Watermarking using Image Mixup
Kassem Kallas, Teddy Furon
It is crucial to protect the intellectual property rights of DNN models prior to their deployment. The DNN should perform two main tasks: its primary task and watermarking task. Th…
ROSE: A RObust and SEcure DNN Watermarking
Kassem Kallas, Teddy Furon
Protecting the Intellectual Property rights of DNN models is of primary importance prior to their deployment. So far, the proposed methods either necessitate changes to internal mo…
CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis
Mauro Barni, Kassem Kallas, Ehsan Nowroozi +1
Last-generation GAN models allow to generate synthetic images which are visually indistinguishable from natural ones, raising the need to develop tools to distinguish fake and natu…
A new Backdoor Attack in CNNs by training set corruption without label poisoning
Mauro Barni, Kassem Kallas, Benedetta Tondi
Backdoor attacks against CNNs represent a new threat against deep learning systems, due to the possibility of corrupting the training set so to induce an incorrect behaviour at tes…
On the Transferability of Adversarial Examples Against CNN-Based Image Forensics
Mauro Barni, Kassem Kallas, Ehsan Nowroozi +1
Recent studies have shown that Convolutional Neural Networks (CNN) are relatively easy to attack through the generation of so-called adversarial examples. Such vulnerability also a…
A Message Passing Approach for Decision Fusion in Adversarial Multi-Sensor Networks
Andrea Abrardo, Mauro Barni, Kassem Kallas +1
We consider a simple, yet widely studied, set-up in which a Fusion Center (FC) is asked to make a binary decision about a sequence of system states by relying on the possibly corru…
A Game-Theoretic Framework for Optimum Decision Fusion in the Presence of Byzantines
Andrea Abrardo, Mauro Barni, Kassem Kallas +1
Optimum decision fusion in the presence of malicious nodes - often referred to as Byzantines - is hindered by the necessity of exactly knowing the statistical behavior of Byzantine…
Optimum Fusion of Possibly Corrupted Reports for Distributed Detection in Multi-Sensor Networks
Andrea Abrardo, Mauro Barni, Kassem Kallas +1
The most common approach to mitigate the impact that the presence of malicious nodes has on the accuracy of decision fusion schemes consists in observing the behavior of the nodes…