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Adversarial attacks against Deep Neural Networks(DNN) have been a crutialtopic ever since \cite{goodfellow} purposed the vulnerability of DNNs. However,most prior works craft adversarial examples in the pixel space, following the$l_p$ norm constraint. In this paper, we give intuitional explain about whycrafting adversarial examples in the latent space is equally efficient andimportant. We purpose a framework for crafting adversarial examples in semanticlatent space based on an pre-trained Variational Auto Encoder from state-of-artStable Diffusion Model\cite{SDM}. We also show that adversarial examplescrafted in the latent space can also achieve a high level of fool rate.However, examples crafted from latent space are often hard to evaluated, asthey doesn't follow a certain $l_p$ norm constraint, which is a big challengefor existing researches. To efficiently and accurately evaluate the adversarialexamples crafted in the latent space, we purpose \textbf{a novel evaluationmatric} based on SSIM\cite{SSIM} loss and fool rate.Additionally, we explainwhy FID\cite{FID} is not suitable for measuring such adversarial examples. Tothe best of our knowledge, it's the first evaluation metrics that isspecifically designed to evaluate the quality of a adversarial attack. We alsoinvestigate the transferability of adversarial examples crafted in the latentspace and show that they have superiority over adversarial examples crafted inthe pixel space.

Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space