Robust Backdoor Detection for Deep Learning via Topological Evolution Dynamics

A backdoor attack in deep learning inserts a hidden backdoor in the model to trigger malicious behavior upon specific input patterns. Existing detection approaches assume a metric space (for either the original inputs or their latent representations) in which normal samples and malicious samples are separable. We show that this assumption has a severe limitation by introducing a novel SSDT (Source-Specific and Dynamic-Triggers) backdoor, which obscures the difference between normal samples and malicious samples. To overcome this limitation, we move beyond looking for a perfect metric space that would work for different deep-learning models, and instead resort to more robust topological constructs. We propose TED (Topological Evolution Dynamics) as a model-agnostic basis for robust backdoor detection. The main idea of TED is to view a deep-learning model as a dynamical system that evolves inputs to outputs. In such a dynamical system, a benign input follows a natural evolution trajectory similar to other benign inputs. In contrast, a malicious sample displays a distinct trajectory, since it starts close to benign samples but eventually shifts towards the neighborhood of attacker-specified target samples to activate the backdoor. Extensive evaluations are conducted on vision and natural language datasets across different network architectures. The results demonstrate that TED not only achieves a high detection rate, but also significantly outperforms existing state-of-the-art detection approaches, particularly in addressing the sophisticated SSDT attack. The code to reproduce the results is made public on GitHub.Comment: 18 pages. To appear in IEEE Symposium on Security and Privacy 202

Assessment of student knowledge integration in learning work and mechanical energy

Work and mechanical energy is a fundamental topic in introductory physics. Studies in existing literature have shown that students have difficulties in understanding work and mechanical energy, particularly the topic of work-energy theorem. To study students’ knowledge integration in learning work and mechanical energy, a conceptual framework model of work and mechanical energy was developed and applied to guide the design of an assessment for measuring students’ level of knowledge integration. Using the assessment, qualitative and quantitative data were collected in two high schools in an eastern Chinese city. The results reveal that the conceptual framework model can effectively represent the students’ knowledge structures at different levels of knowledge integration. In addition, the assessment is shown effective in identifying unique features of knowledge integration, including context dependence and fragmentation of knowledge components, memorization-based problem-solving strategies, and lack of meaningful connections between work and change in kinetic energy. The conceptual framework of work and mechanical energy and assessment results can provide useful information to facilitate instructional designs to promote knowledge integration