The paper investigates whether spiking neural networks (SNNs) are more resistant to model stealing attacks than conventional artificial neural networks (ANNs). By benchmarking four common attacks on MNIST, Fashion‑MNIST, and CIFAR‑10, the authors find that SNNs consistently yield surrogates with 10%–20% lower agreement and 15%–25% lower adversarial transferability compared to ANNs. The authors attribute this resilience to the spike‑based, time‑dependent dynamics of SNNs, which make their decision boundaries harder for ANN‑based surrogates to approximate.
By Ayana Moshruba, Maryam Parsa
The paper investigates model watermarking for hyperdimensional computing (HDC) classifiers, adapting backdoor-based embedding to three prototype‑learning mechanisms. Across four benchmarks, error‑driven models (AdaptHD and OnlineHD) support ownership verification with minimal accuracy loss, while single‑pass Vanilla HDC requires higher trigger exposure and incurs larger accuracy penalties. The study also shows that watermarks can be overwritten, survive certain stress tests on FPGA, but may be lost during extraction or severe stress, highlighting both feasibility and limitations of HDC watermarking.
By Hamed Poursiami, Farshad Soleiman, Ahmad Tahmasivand, Guojing Cong, Thomas Potok, Khaled N. Khasawneh, Kai Zeng, Maryam Parsa
arXiv:2607. 27990v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) communicate through sparse binary spike events rather than dense activations, enabling energy-efficient inference on neuromorphic hardware and motivating their use in always-on, battery-powered edge systems.
By Spyridon Raptis, Haralampos-G. Stratigopoulos
SpikeQ introduces a spike‑guided post‑training quantization framework for spiking neural networks, selecting weight levels by simulating downstream neurons to minimize spike errors and designing a reset‑aware grid for pre‑fire membrane potential quantization that avoids spike flips. The method outperforms existing grids on seven image‑processing benchmarks, maintaining full‑precision accuracy with 2‑bit pre‑fire potentials and staying within 2.59 points of full precision at 4 bits while drastically reducing memory and energy usage.
By Kai Sun, Peibo Duan, Zhanglu Yan, Guowei Zhang, Yongsheng Huang, Levin Kuhlmann
arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.
By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara
arXiv:2504.14015v2 Announce Type: replace-cross
Abstract: We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study express...
By Dominik Dold, Philipp Christian Petersen
arXiv:2603. 23171v3 Announce Type: replace-cross Abstract: Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent.
By Toluwani Aremu, Daniil Ognev, Samuele Poppi, Nils Lukas
NeuronSpark is a self‑attention‑free language model that uses input‑dependent spiking membrane dynamics, allowing each neuron’s decay rate, gain, and firing threshold to adapt to the current token. This selective spiking approach enables billion‑parameter pretraining from random initialization without relying on backbone transfer, conversion, or distillation, achieving competitive zero‑shot performance and a fast–slow hierarchy. On a 1.1B‑parameter, 2.1B‑token training run, NeuronSpark attains a 40.0 eight‑task zero‑shot average, surpasses comparable‑budget Pythia/Mamba checkpoints, and demonstrates significant energy and cycle reductions on FPGA implementations.
By Zhengzheng Tang
TwinMark is a watermarking scheme that embeds a single SHAKE128 secret into a vision model using two complementary linear functionals of model-output summaries: a covariance projector (cov‑Feat) and a class‑conditional Fisher‑aligned linear carrier (cc‑FALC). These readouts cover both classifier APIs attacked by KL knowledge distillation and representation‑only hosts attacked by feature‑matching distillation, each providing a teacher‑measurable a posteriori certificate. Across 13 attacks on datasets such as CIFAR‑10, CIFAR‑100, and Mini‑ImageNet, TwinMark remains detectable on every post‑attack model that retains task utility, survives cross‑architecture distillation onto ResNet‑18/50, VGG‑16, and MobileNet‑V3, and can be ported to GNSS few‑shot, VOC detection, ISIC segmentation, and STL‑10 SimCLR.
By Redwanul Karim, Tobias Feigl, Christopher Mutschler, Felix Ott
arXiv:2609.32241v2 Announce Type: replace-cross
Abstract: Neural image watermarks can be forged by extracting watermark-bearing residuals from released images and transferring them to unrelated conte...
By Ziping Dong, Qi Li, Xinchao Wang
The paper investigates latent‑space watermarking using pretrained generators, where a watermark encoder selects latent inputs based on a message and secret key to produce outputs with a specified conditional distribution. For finite alphabets, it derives inner and outer bounds on the rate–key trade‑off and characterizes the capacity region when the generator’s output uniquely determines the latent distribution. The study extends to jointly Gaussian models, identifies key sufficient statistics, optimally allocates secret‑key resources across modes, and analyzes robustness against regeneration attacks, providing compound capacity results and decay rates for repeated attacks.
By Jinwan Jeon, Minju Lee, Sung Hoon Lim
arXiv:2608. 19232v1 Announce Type: cross Abstract: Spiking point cloud networks usually scan space in a fixed, input-agnostic order, which leaves the most distinctive resource of spiking computation, the temporal evolution of the membrane potential, unused as a locus of decision-making.
By Akarsh Jain, Arya Pawa, Ayush Debnath, Smera Rawal, Sayeed Shafayet Chowdhury