Konrad Rieck
Institution: Technische Universität Berlin & BIFOLD / CASA
Research Hub(s):
Hub A: Kryptographie der Zukunft
Hub B: Eingebettete Sicherheit
Hub C: Sichere Systeme
E-Mail: Rieck@tu-berlin.de
Website: https://mlsec.org
Twitter: @mlsec
Publikationen:
Misleading Authorship Attribution of Source Code using Adversarial Learning Misleading Deep-Fake Detection with GAN Fingerprints TagVet: Vetting Malware Tags using Explainable Machine Learning Thieves in the Browser: Web-based Cryptojacking in the Wild Dancer in the Dark: Synthesizing and Evaluating Polyglots for Blind Cross-Site Scripting New Kid on the Web: A Study on the Prevalence of WebAssembly in the Wild No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning Backdooring and Poisoning Neural Networks with Image-Scaling Attacks Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning Evaluating Explanation Methods for Deep Learning in Computer Security LogPicker: Strengthening Certificate Transparency Against Covert Adversaries Dos and Don'ts of Machine Learning in Computer Security LaserShark: Establishing Fast, Bidirectional Communication into Air-Gapped Systems Machine Unlearning of Features and Labels Spying through Virtual Backgrounds of Video Calls
