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Found 12 papers, 2 papers with code
Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems
In this paper, we present the first large-scale study on the stealthiness of adversarial samples used in the attacks against deep learning.
Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs
For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold.
Semantic Clustering based Deduction Learning for Image Recognition and Classification
The paper proposes a semantic clustering based deduction learning by mimicking the learning and thinking process of human brains.
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency
To this end, Argos first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree.
DeepDyve: Dynamic Verification for Deep Neural Networks
The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much.
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval
Recurrent Neural Network (RNN)-based sequential recommendation is a popular approach that utilizes users' recent browsing history to predict future items.
Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples
Deep neural networks (DNNs) are shown to be susceptible to adversarial example attacks.
On Functional Test Generation for Deep Neural Network IPs
Considering the large amount of training data and know-how required to generate the network, it is more practical to use third-party DNN intellectual property (IP) cores for many designs.
On Configurable Defense against Adversarial Example Attacks
Machine learning systems based on deep neural networks (DNNs) have gained mainstream adoption in many applications.
I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators
Deep learning has become the de-facto computational paradigm for various kinds of perception problems, including many privacy-sensitive applications such as online medical image analysis.
Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks
Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes.
Learning Social Circles in Ego Networks based on Multi-View Social Graphs
Based on extensive automatic and manual experimental evaluations, we deliver two major findings: first, multi-view clustering techniques perform better than common single-view clustering techniques, which only use one view or naively integrate all views for detection, second, the standard multi-view clustering technique is less robust than our modified technique, which selectively transfers information across views based on an assumption that sparse network structures are (potentially) incomplete.
