论文标题
关于gan的质量措施的鲁棒性
On the Robustness of Quality Measures for GANs
论文作者
论文摘要
这项工作评估了生成模型的质量度量的鲁棒性，例如INPECTION评分（IS）和Fréchet成立距离（FID）。类似于深层模型对各种对抗性攻击的脆弱性，我们表明，加性像素扰动也可以操纵此类指标。我们的实验表明，一个人可以生成分数很高但感知质量低的图像分布。相反，人们可以优化小型不察觉的扰动，当添加到现实世界图像中时，它们的分数会恶化。我们进一步将评估扩展到生成模型本身，包括最先进的网络样式。我们显示了生成模型和FID的脆弱性，反对潜在空间中的累加扰动。最后，我们表明，通过简单地以强大的成立替换标准构成，可以鲁棒化。我们通过广泛的实验来验证鲁棒度量的有效性，表明它对操纵更为强大。
This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fréchet Inception Distance (FID). Analogous to the vulnerability of deep models against a variety of adversarial attacks, we show that such metrics can also be manipulated by additive pixel perturbations. Our experiments indicate that one can generate a distribution of images with very high scores but low perceptual quality. Conversely, one can optimize for small imperceptible perturbations that, when added to real world images, deteriorate their scores. We further extend our evaluation to generative models themselves, including the state of the art network StyleGANv2. We show the vulnerability of both the generative model and the FID against additive perturbations in the latent space. Finally, we show that the FID can be robustified by simply replacing the standard Inception with a robust Inception. We validate the effectiveness of the robustified metric through extensive experiments, showing it is more robust against manipulation.
