5 citations · 11 across the 3 of their papers we have counts for
5 papers
Misleading Deep-Fake Detection with GAN Fingerprints
Vera Wesselkamp, Konrad Rieck, Daniel Arp +1
Generative adversarial networks (GANs) have made remarkable progress in synthesizing realistic-looking images that effectively outsmart even humans. Although several detection meth…
Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification
Erwin Quiring, Lukas Pirch, Michael Reimsbach +2
Machine learning-based systems for malware detection operate in a hostile environment. Consequently, adversaries will also target the learning system and use evasion attacks to byp…
Evaluating Explanation Methods for Deep Learning in Security
Alexander Warnecke, Daniel Arp, Christian Wressnegger +1
Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machi…
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
Ambra Demontis, Marco Melis, Battista Biggio +6
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However,…
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking
Erwin Quiring, Daniel Arp, Konrad Rieck
Machine learning is increasingly used in security-critical applications, such as autonomous driving, face recognition and malware detection. Most learning methods, however, have no…