A Comprehensive Review of Adversarial Learning and Impact of Unsharing Weights Across Classes

Security Enhancement of mmWave MIMO Wireless Communication System Using Adversarial Training

“…In each iteration, it recalculates the perturbation using the sign of the gradients of the loss function with respect to the perturbed input from the previous iteration. After initialization of adversarial example at 't' th iteration, that is, x * 0 = x t , the adversarial instance at 't + 1'th iteration (x * t+1 ) can be described by (2) [18].…”

“…In each iteration, it recalculates the perturbation using the sign of the gradients of the loss function with respect to the perturbed input from the previous iteration. After initialization of adversarial example at ‘ t ’ th iteration, that is, $$$ {x}_0^{\ast }={x}_t $$$, the adversarial instance at ‘ t + 1’th iteration ($$$ {x}_{t+1}^{\ast } $$$) can be described by (2) [18]. $$$$ {x}_{t+1}^{\ast }={x}_t^{\ast }+\alpha .\mathit{\operatorname{sign}}\left({\nabla}_xJ\left(\theta, {x}_t^{\ast },\mathrm{y}\right)\right).$$…”