universal adversarial perturbations
时间: 2023-04-15 18:03:08 浏览: 89
通用对抗扰动(universal adversarial perturbations)是指一种可以对多个图像进行攻击的对抗扰动,即使这些图像属于不同的类别。这种扰动可以通过对训练数据进行反向传播来生成,然后添加到测试数据中,从而使分类器产生错误的预测结果。通用对抗扰动是对抗样本攻击的一种重要形式,可以用于测试和评估机器学习算法的鲁棒性和安全性。
相关问题
写出universal adversarial pertubation 的pytorch代码
Universal adversarial perturbation(UAP)的pytorch代码如下:import torchdef UAP(model, x, y_true, epsilon):
x_adv = x.clone().detach()
x_adv.requires_grad = True
y_pred = model(x_adv)
loss = torch.nn.CrossEntropyLoss()(y_pred, y_true)
loss.backward()
x_adv.data = x_adv - epsilon * torch.sign(x_adv.grad)
x_adv.data = torch.clamp(x_adv.data, 0, 1)
x_adv.grad.zero_()
return x_adv
Adversarial loss
Adversarial loss, also known as GAN loss, is a loss function used in Generative Adversarial Networks (GANs). GANs consist of two neural networks: a generator network that generates fake data, and a discriminator network that distinguishes between the fake data generated by the generator and the real data. The goal of the generator is to generate data that is indistinguishable from real data, while the goal of the discriminator is to correctly distinguish between real and fake data.
The adversarial loss is used to train the generator network. It is calculated by feeding the generator's output into the discriminator and then using the discriminator's output as the loss. The generator seeks to minimize this loss, while the discriminator seeks to maximize it. This creates a game-like scenario where the generator and discriminator are in competition with each other, which leads to the generator improving its ability to generate realistic data.