Search Results for author:
Found 6 papers, 3 papers with code
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.
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.
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.
