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Preprocessing defenses fail to recover adversarially perturbed inputs in depthwise-separable CNNs, but they reveal a new detection opportunity through measurable output divergence.
Privacy noise in federated learning can severely compromise the detection of rare attacks, challenging the assumption that privacy and robustness can be optimized independently.
Stealth tactics dominate healthcare cyber threats, with defense evasion accounting for 15-20% of techniques, while traditional detection methods fail to keep pace.