ORBITAL: Optimization of Robust Brain--Computer Inference Through Architecture-Aware Low-Precision Compression
Compact electroencephalography (EEG) decoders enable embedded brain--computer interfaces (BCIs), but compression can alt
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← 返回所有主题Evaluating Federated Vision-Language and Vision-Only Models for Privacy-Aware Breast Cancer Imaging
Artificial intelligence (AI) shows promise for breast imaging, but limited labeled data, institutional data fragmentatio
BUSTA: Teaching Small Language Models to Reason About Binary Execution
Large Language Models (LLM) are increasingly applied to Reverse Engineering (RE) workflows. RE, however, differs from co
Privately Clipping Heavy-Tailed Data
Summing vectors is a basic task in differentially private data analysis. Standard private sum algorithms clip vectors to
Information-Theoretic Secure Aggregation With Heterogeneous Client Quantization
In federated learning (FL), secure aggregation enables a server to compute the sum of local model updates of $N$ clients
Adversary Brains: Hypothesis-Based Logical Signatures for Threat Actor Detection with LLMs
An adversary brain is an LLM-based model of a threat actor's behavior, held as an evolving logical signature: a hypothes
Intellectual Property Protection of Hyperdimensional Computing Classifiers: Black-Box Ownership Verification via Model Watermarking
Hyperdimensional computing (HDC) offers efficient learning and inference for resource-constrained systems, yet ownership
A Certified Robustness Framework for Backdoor Poisoning Attacks using $f$-Divergences
Machine learning systems trained on untrusted data are vulnerable to data and backdoor poisoning attacks, in which an ad
Attack logics, not outputs: Concept-based Property Attacks for Shortcut Elimination in Neural Networks
Deep neural networks (NNs) for computer vision are vulnerable to adversarial attacks, such that minuscule malicious chan
BE-CTDE: Blockchain-Enabled Multi-Agent Reinforcement Learning with Anomaly Detection and Token Economics
Multi-agent reinforcement learning supports cooperative decision-making in Internet of Things systems, but strategic dev
Ranking Is Not Deciding: Benchmark Shortcuts and Operating-Point Collapse in Query-Time RAG Poisoning Defenses
Query-time filters that screen each retrieved document before it reaches the generator are the dominant published defens
EdgeDeID: A 0.6B Clinical De-identifier Trained Without Real Patient Data
De-identifying clinical notes with a hosted language model discloses the information the step is meant to protect. We pr
Trust Mechanisms for Offline Generative AI at the Edge: A Connectivity-Regime Framework and an Empirical Study on a Raspberry Pi 5
Generative AI and large language models (LLMs) are moving into the Internet of Things, yet most deployments assume cloud
VulDec: Vulnerability Detection in Binaries via Neural Decompilation
Automated vulnerability detection in binary executables is challenging due to the lack of source code and the limited se
Extraction meets Distillation - cryptanalytic techniques to recover parameters from a Deep Neural Network
We study fully connected neural-network reconstruction by combining cryptanalytic weight-direction extraction with learn
Combinatorial Lock for Trojan-Resilient AI Engines via Hash-Assisted Spectral Filtering
Multi-trigger backdoor attacks, in which heterogeneous trigger mechanisms (e.g., patch, blend, warping, and the latent-d
Holo-NN: Holographic Nearest Neighbor Search via Compressive Sensing
In the age of AI, privacy-preserving approximate nearest neighbor (ANN) search over high-dimensional embeddings has emer
Trusting the Tool: Asymmetric Deference of LLM Vulnerability Auditors to Static-Analysis Evidence
Pipelines that combine large language models (LLMs) with static analysis let the model consume the analyser's output as
Depth-Dependent Safety Geometry in Vision-Language Models: Layer-Wise Localization and Bidirectional Refusal Control
Vision-Language Models (VLMs) are widely deployed, yet the internal mechanisms behind their safety behavior remain poorl
Curvature-Aware Clipping: Mitigating Bias in DPSGD via Approximate Second-Order Information
Differentially Private Stochastic Gradient Descent (DPSGD) has become the de facto standard for training deep neural net