SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting

Time-series forecasting often operates under tight power and latency budgets in fields like traffic management, industrial condition monitoring, and on-device sensing. These applications frequently require near real-time responses and low energy consumption on edge devices. Spiking neural networks (SNNs) offer event-driven computation and ultra-low power by exploiting temporal sparsity and multiplication-free computation. Yet existing SNN-based time-series forecasters often inherit complex transformer blocks, thereby losing much of the efficiency benefit. To solve the problem, we propose SpikySpace, a spiking state-space model (SSM) that reduces the quadratic cost in the attention block to linear time via selective scanning. Further, we replace dense SSM updates with sparse spike trains and execute selective scans only on spike events, thereby avoiding dense multiplications while preserving the SSM's structured memory. Because complex operations such as exponentials and divisions are costly on neuromorphic chips, we introduce simplified approximations of SiLU and Softplus to enable a neuromorphic-friendly model architecture. In matched settings, SpikySpace reduces estimated energy consumption by 98.73% and 96.24% compared to two state-of-the-art transformer based approaches, namely iTransformer and iSpikformer, respectively. In standard time series forecasting datasets, SpikySpace delivers competitive accuracy while substantially reducing energy cost and memory traffic. As the first full spiking state-space model, SpikySpace bridges neuromorphic efficiency with modern sequence modeling, marking a practical and scalable path toward efficient time series forecasting systems.

Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence

Deep neural networks are vulnerable to adversarial attacks. In this paper, we take the role of investigators who want to trace the attack and identify the source, that is, the particular model which the adversarial examples are generated from. Techniques derived would aid forensic investigation of attack incidents and serve as deterrence to potential attacks. We consider the buyers-seller setting where a machine learning model is to be distributed to various buyers and each buyer receives a slightly different copy with same functionality. A malicious buyer generates adversarial examples from a particular copy $\mathcal{M}_i$ and uses them to attack other copies. From these adversarial examples, the investigator wants to identify the source $\mathcal{M}_i$. To address this problem, we propose a two-stage separate-and-trace framework. The model separation stage generates multiple copies of a model for a same classification task. This process injects unique characteristics into each copy so that adversarial examples generated have distinct and traceable features. We give a parallel structure which embeds a ``tracer'' in each copy, and a noise-sensitive training loss to achieve this goal. The tracing stage takes in adversarial examples and a few candidate models, and identifies the likely source. Based on the unique features induced by the noise-sensitive loss function, we could effectively trace the potential adversarial copy by considering the output logits from each tracer. Empirical results show that it is possible to trace the origin of the adversarial example and the mechanism can be applied to a wide range of architectures and datasets.