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Found 17 papers, 3 papers with code
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation
Specifically, our approach identifies the Principal Adversarial Domains (PADs), i. e., a combination of features of the adversarial examples from different attacks, which possesses large coverage of the entire adversarial feature space.
LSP Framework: A Compensatory Model for Defeating Trigger Reverse Engineering via Label Smoothing Poisoning
Based on this paradigm, we propose a new perspective to defeat trigger reverse engineering by manipulating the classification confidence of backdoor samples.
Understanding Heterophily for Graph Neural Networks
Firstly, we show that by applying a GC operation, the separability gains are determined by two factors, i. e., the Euclidean distance of the neighborhood distributions and $\sqrt{\mathbb{E}\left[\operatorname{deg}\right]}$, where $\mathbb{E}\left[\operatorname{deg}\right]$ is the averaged node degree.
RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images
In this paper, we propose the first deep forgery detection approach for fingerprint images, which combines unique ridge features of fingerprint and generation artifacts of the GAN-generated images, to the best of our knowledge.
Common Knowledge Learning for Generating Transferable Adversarial Examples
Specifically, to reduce the model-specific features and obtain better output distributions, we construct a multi-teacher framework, where the knowledge is distilled from different teacher architectures into one student network.
Heterophily-Aware Graph Attention Network
In this paper, we firstly propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i. e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, which enable nodes to acquire appropriate information from distinct neighbors.
Binary Graph Convolutional Network with Capacity Exploration
The current success of Graph Neural Networks (GNNs) usually relies on loading the entire attributed graph for processing, which may not be satisfied with limited memory resources, especially when the attributed graph is large.
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding
Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.
Diverse Message Passing for Attribute with Heterophily
Based on this attribute homophily rate, we propose a Diverse Message Passing (DMP) framework, which specifies every attribute propagation weight on each edge.
Exploring Transferable and Robust Adversarial Perturbation Generation from the Perspective of Network Hierarchy
In this paper, we explore effective mechanisms to boost both of them from the perspective of network hierarchy, where a typical network can be hierarchically divided into output stage, intermediate stage and input stage.
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate
This portion of distortions, which is induced by unnecessary modifications and lack of proper perceptual distortion constraint, is the target of the proposed framework.
Bi-GCN: Binary Graph Convolutional Network
Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning.
Fake Generated Painting Detection via Frequency Analysis
With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts.
Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism
Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientific challenges and application potentials, where localizing attributes is a crucial stage but not well handled.
Fake Colorized Image Detection
Based on our observations, i. e., potential traces in the hue, saturation, dark and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram based Fake Colorized Image Detection (FCID-HIST) and Feature Encoding based Fake Colorized Image Detection (FCID-FE).
Multimedia
Binarized Mode Seeking for Scalable Visual Pattern Discovery
Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset.
Image Deblurring via Extreme Channels Prior
The proposed method takes advantage of both Bright and Dark Channel Prior.
