IMPOSITION: Implicit Backdoor Attack through Scenario Injection

This paper presents a novel backdoor attack called IMPlicit BackdOor Attack through Scenario InjecTION (IMPOSITION) that does not require direct poisoning of the training data. Instead, the attack leverages a realistic scenario from the training data as a trigger to manipulate the model's output during inference. This type of attack is particularly dangerous as it is stealthy and difficult to detect. The paper focuses on the application of this attack in the context of Autonomous Driving (AD) systems, specifically targeting the trajectory prediction module. To implement the attack, we design a trigger mechanism that mimics a set of cloned behaviors in the driving scene, resulting in a scenario that triggers the attack. The experimental results demonstrate that IMPOSITION is effective in attacking trajectory prediction models while maintaining high performance in untargeted scenarios. Our proposed method highlights the growing importance of research on the trustworthiness of Deep Neural Network (DNN) models, particularly in safety-critical applications. Backdoor attacks pose a significant threat to the safety and reliability of DNN models, and this paper presents a new perspective on backdooring DNNs. The proposed IMPOSITION paradigm and the demonstration of its severity in the context of AD systems are significant contributions of this paper. We highlight the impact of the proposed attacks via empirical studies showing how IMPOSITION can easily compromise the safety of AD systems.