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…
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…
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),…