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Found 13 papers, 3 papers with code
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning
We focus on the fact that the state-of-the-art poisoning attack on GCL tends to mainly add adversarial edges to create poisoned graphs, which means that pruning edges is important to sanitize the graphs.
Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer components, and wireless domain constraints.
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations
Specifically, VoteTRANS detects adversarial text by comparing the hard labels of input text and its transformation.
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression
Experimental results demonstrate that NetFlick can successfully deteriorate the performance of video compression frameworks in both digital- and physical-settings and can be further extended to attack downstream video classification networks.
Text Revealer: Private Text Reconstruction via Model Inversion Attacks against Transformers
Our attacks faithfully reconstruct private texts included in training data with access to the target model.
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression
In this paper, we conduct the first systematic study for adversarial attacks on deep learning-based video compression and downstream classification systems.
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
However, this algorithm does not protect edges (friendships) in a social graph, hence cannot protect user privacy in unattributed graphs.
SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text
In terms of misclassified texts, a classifier handles the texts with both incorrect predictions and adversarial texts, which are generated to fool the classifier, which is called a victim.
Machine Translated Text Detection Through Text Similarity with Round-Trip Translation
However, a round-trip translated text is significantly different from the original text or a translated text using a strange translator.
TransMIA: Membership Inference Attacks Using Transfer Shadow Training
Transfer learning has been widely studied and gained increasing popularity to improve the accuracy of machine learning models by transferring some knowledge acquired in different training.
Identifying Adversarial Sentences by Analyzing Text Complexity
Attackers create adversarial text to deceive both human perception and the current AI systems to perform malicious purposes such as spam product reviews and fake political posts.
Detecting Machine-Translated Text using Back Translation
The existing methods detected a machine-translated text only using the text's intrinsic content, but they are unsuitable for classifying the machine-translated and human-written texts with the same meanings.
Detecting Machine-Translated Paragraphs by Matching Similar Words
We have developed a method matching similar words throughout the paragraph and estimating the paragraph-level coherence, that can identify machine-translated text.
