Abstract / truncated to 115 words (read the full abstract)

The use of machine-learning for multimedia forensics is gaining more and more consensus, especially due to the amazing possibilities offered by modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceed those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques. However, the inherent vulnerability and fragility of machine learning architectures pose new serious security threats, hindering the use of these tools in security-oriented applications, and, among them, multimedia forensics. The analysis of the security of machine learning-based techniques in the presence of an adversary attempting to impede the forensic analysis, and the development of new solutions capable to improve the security of such techniques ... toggle 20 keywords

image forensics – machine learning – deep learning – multimedia forensics – multimedia security – adversarial machine learning – adversarial multimedia forensics – information forensics and security – adversarial learning – deep learning for multimedia forensics – machine learning for multimedia forensics – cybersecurity – jpeg quality factor estimation – forensics and counter-forensics – manipulation detection – secure classification – security of machine learning – security of deep learning – attack transferability – secure classification

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