5 papers
Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization
Xinhai Zou, Chang Zhao, Alireza Aghabagherloo +3
Gradient-based adversarial attacks remain a dominant threat to deep neural networks (DNNs), as they exploit gradient information to efficiently optimize adversarial perturbations.…
Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
Alireza Aghabagherloo, Aydin Abadi, Sumanta Sarkar +2
The accuracy and robustness of machine learning models against adversarial attacks are significantly influenced by factors such as training data quality, model architecture, the tr…
Mining Power Destruction Attacks in the Presence of Petty-Compliant Mining Pools
Roozbeh Sarenche, Svetla Nikova, Bart Preneel
Bitcoin's security relies on its Proof-of-Work consensus, where miners solve puzzles to propose blocks. The puzzle's difficulty is set by the difficulty adjustment mechanism (DAM),…
Bitcoin under Volatile Block Rewards: How Mempool Statistics Can Influence Bitcoin Mining
Roozbeh Sarenche, Alireza Aghabagherloo, Svetla Nikova +1
The security of Bitcoin protocols is deeply dependent on the incentives provided to miners, which come from a combination of block rewards and transaction fees. As Bitcoin experien…
Commitment Attacks on Ethereum's Reward Mechanism
Roozbeh Sarenche, Ertem Nusret Tas, Barnabe Monnot +2
Validators in permissionless, large-scale blockchains, such as Ethereum, are typically payoff-maximizing, rational actors. Ethereum relies on in-protocol incentives, like rewards f…