Kun Cheng

Kun Cheng, Lei Yu, Zhijun Tu et al.

Recent advances indicate that diffusion models hold great promise in image super-resolution. While the latest methods are primarily based on latent diffusion models with convolutional neural networks, there are few attempts to explore transformers, which have demonstrated remarkable performance in image generation. In this work, we design an effective diffusion transformer for image super-resolution (DiT-SR) that achieves the visual quality of prior-based methods, but through a training-from-scratch manner. In practice, DiT-SR leverages an overall U-shaped architecture, and adopts a uniform isotropic design for all the transformer blocks across different stages. The former facilitates multi-scale hierarchical feature extraction, while the latter reallocates the computational resources to critical layers to further enhance performance. Moreover, we thoroughly analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module, enhancing the model's capacity to process distinct frequency information at different time steps. Extensive experiments demonstrate that DiT-SR outperforms the existing training-from-scratch diffusion-based SR methods significantly, and even beats some of the prior-based methods on pretrained Stable Diffusion, proving the superiority of diffusion transformer in image super-resolution.

Kun Cheng, Yuan Zhou, Bihuan Chen et al.

Due to the broad attack surface and the lack of runtime protection, potential safety and security threats hinder the real-life adoption of autonomous vehicles. Although efforts have been made to mitigate some specific attacks, there are few works on the protection of the self-driving system. This paper presents a decentralized self-protection framework called Guardauto to protect the self-driving system against runtime threats. First, Guardauto proposes an isolation model to decouple the self-driving system and isolate its components with a set of partitions. Second, Guardauto provides self-protection mechanisms for each target component, which combines different methods to monitor the target execution and plan adaption actions accordingly. Third, Guardauto provides cooperation among local self-protection mechanisms to identify the root-cause component in the case of cascading failures affecting multiple components. A prototype has been implemented and evaluated on the open-source autonomous driving system Autoware. Results show that Guardauto could effectively mitigate runtime failures and attacks, and protect the control system with acceptable performance overhead.

Kun Cheng, Yuebin Bai, Yuan Zhou et al.

Autonomous driving vehicles (ADVs) are implemented with rich software functions and equipped with many sensors, which in turn brings broad attack surface. Moreover, the execution environment of ADVs is often open and complex. Hence, ADVs are always at risk of safety and security threats. This paper proposes a fast method called Multi-IF, using multiple invocation features of system calls to detect anomalies in self-driving systems. Since self-driving functions take most of the computation resources and upgrade frequently, Multi-IF is designed to work under such resource constraints and support frequent updates. Given the collected sequences of system calls, the combination of different syntax patterns is used to analyze and construct feature vectors of those sequences. By taking the feature vectors as inputs, one-class support vector machine is adopted to determine whether the current sequence of system calls is abnormal, which is trained with the feature vectors from the normal sequences. The evaluations on both simulated and real data prove that the proposed method is effective in identifying the abnormal behavior after minutes of feature extraction and training. Further comparisons with the existing methods on the ADFA-LD data set also validate that the proposed approach achieves a higher accuracy with less time overhead.