Empirical evaluations on three benchmark datasets demonstrate that our RS&V could detect the textual adversarial examples more successfully than the existing detection methods while maintaining the high classification accuracy on benign samples. Other words (aforementioned, hereinafter) are used mainly in very formal texts, for example legal documents. The proposed RS&V is generally applicable to any existing neural networks without modification on the architecture or extra training, and it is orthogonal to prior work on making the classification network itself more robust. Textual aids are tools or materials that provide support and facilitate understanding of texts. For more examples that are common in social science publications, see the DIAGRAM Centers Image Description Guidelines. Based on this observation, we propose a novel textual adversarial example detection method, termed Randomized Substitution and Vote (RS&V), which votes the prediction label by accumulating the logits of k samples generated by randomly substituting the words in the input text with synonyms. Textual aids are educational instruments, could be written texts, or printed texts and other ways of emphasizing the essential phrases, thoughts, graphs, and /or images. We identify that we could destroy such mutual interaction and eliminate the adversarial perturbation by randomly substituting a word with its synonyms. In this work, we treat the optimization process for synonym substitution based textual adversarial attacks as a specific sequence of word replacement, in which each word mutually influences other words. adversarial training, input transformations, detection, etc.
Correspondingly, various defense methods are proposed to mitigate the threat of textual adversarial examples, e.g. Just like architects plan the structure of a building, authors plan the.
Readers can analyze the structure of all sorts of texts, such as narratives and informational texts, but the analytical techniques vary depending on the text type. A line of work has shown that natural text processing models are vulnerable to adversarial examples. Text structure analysis is the process of breaking down a text to examine how the author organized it.