论文标题
坚固的未配对的单像面孔超分辨率
Robust Unpaired Single Image Super-Resolution of Faces
论文作者
论文摘要
我们提出了针对特定于特定于特定的单图超分辨率（SISR）方法的对抗性攻击。现有的攻击，例如快速梯度标志方法（FGSM）或预计的梯度下降（PGD）方法，是快速但无效，或有效但在这些网络上有效的放缓。通过仔细检查用于训练此类网络的MSE损失，不同的降解下的痕迹，我们能够识别其可参数属性。我们利用该属性提出了能够找到最佳降解（有效）的副攻击，而无需多个梯度呈坡度的步骤（快速）。我们的实验表明，所提出的方法能够实现与诸如FGSM和PGD之类的thea theart对抗性攻击，以实现更好的速度与有效性权衡，以实现未配对的面部和特定于班级的SISR的任务。
We propose an adversarial attack for facial class-specific Single Image Super-Resolution (SISR) methods. Existing attacks, such as the Fast Gradient Sign Method (FGSM) or the Projected Gradient Descent (PGD) method, are either fast but ineffective, or effective but prohibitively slow on these networks. By closely inspecting the surface that the MSE loss, used to train such networks, traces under varying degradations, we were able to identify its parameterizable property. We leverage this property to propose an adverasrial attack that is able to locate the optimum degradation (effective) without needing multiple gradient-ascent steps (fast). Our experiments show that the proposed method is able to achieve a better speed vs effectiveness trade-off than the state-of-theart adversarial attacks, such as FGSM and PGD, for the task of unpaired facial as well as class-specific SISR.
