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Found 8 papers, 3 papers with code
On Breaking Deep Generative Model-based Defenses and Beyond
The idea is to view the inversion phase as a dynamical system, through which we extract the gradient with respect to the input by tracing its recent trajectory.
VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprints
(2) We introduce a novel approach using less private samples to enhance the performance of ownership testing.
Self-information Domain-based Neural CSI Compression with Feature Coupling
The proposed SD-CsiNet projects the raw CSI onto a self-information matrix in the newly-defined self-information domain, extracts both temporal and spatial features of the self-information matrix, and then couples these two features for effective compression.
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective
Given the fixed point equation (FPE) derived from the variational inference on the Markov random fields, the deep GNNs, including JKNet, GCNII, DGCN, and the classical GNNs, such as GCN, GAT, and APPNP, can be regarded as different approximations of the FPE.
Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics
Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance.
A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication
To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals.
Unsupervised Classification of Street Architectures Based on InfoGAN
Furthermore, we draw a series of heuristic conclusions from the intrinsic information hidden in true images.
A Deep, Information-theoretic Framework for Robust Biometric Recognition
Deep neural networks (DNN) have been a de facto standard for nowadays biometric recognition solutions.
