Recent researches indicate deep learning models are vulnerable to adversarial attacks. Backdoor attack, also called trojan attack, is a variant of adversarial attacks. An malicious attacker can inject backdoor to models in training phase. As a result, the backdoor model performs normally on clean samples and can be triggered by a backdoor pattern to recognize backdoor samples as a wrong target label specified by the attacker. However, the vanilla backdoor attack method causes a measurable difference between clean and backdoor samples in latent space. Several state-of-the-art defense methods utilize this to identify backdoor samples. In this paper, we propose a novel backdoor attack method called SimTrojan, which aims to inject backdoor in models stealthily. Specifically, SimTrojan makes clean and backdoor samples have indistinguishable representations in latent space to evade current defense methods. Experiments demonstrate that SimTrojan achieves a high attack success rate and is undetectable by state-of-the-art defense methods. The study suggests the urgency of building more effective defense methods.

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