Training machine learning (ML) models is expensive in terms of computational power,
amounts of labeled data and human expertise. Thus, ML models constitute business value …

Deep neural networks have had enormous impact on various domains of computer science,
considerably outperforming previous state of the art machine learning techniques. To …

Deep learning has been achieving top levels of performance in many tasks. However, since
it is costly to train a deep learning model, neural network models must be treated as …

Significant progress has been made with deep neural networks recently. Sharing trained
models of deep neural networks has been a very important in the rapid progress of research …

Deep neural networks (DNNs) have become state-of-the-art in many application domains.
The increasing complexity and cost for building these models demand means for protecting …

Watermarking is a commonly used strategy to protect creators' rights to digital images,
videos and audio. Recently, watermarking methods have been extended to deep learning …

Deep Neural Network (DNN) watermarking is a method for provenance verification of DNN
models. Watermarking should be robust against watermark removal attacks that derive a …

The state-of-the-art performance of deep learning models comes at a high cost for
companies and institutions, due to the tedious data collection and the heavy processing …

Machine learning is increasingly used in securitycritical applications, such as autonomous
driving, face recognition, and malware detection. Most learning methods, however, have not …

Watermarking algorithms have been introduced in the past years to protect deep learning
models against unauthorized re-distribution. We investigate the robustness and reliability of …