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Attack ensembles optimized for minimum-norm strategies can provide a more accurate and flexible evaluation of adversarial robustness than traditional fixed-budget methods.
Slash malware detection labeling costs by 90% using combined active and semi-supervised learning, without sacrificing performance.
Colluding federated learning participants can no longer hide: BlackCATT enables robust black-box traitor tracing even with sophisticated attacks and complex model architectures.
