Adversarial text generation method and system for black box text classification model and medium
A text classification and model technology, applied in text database clustering/classification, unstructured text data retrieval, natural language data processing, etc., can solve the problem that the attacker cannot know the internal structure and parameters of the victim model, and achieve smoothness The effect of easy guarantee and high attack success rate
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0046] Example embodiment of the present embodiment, a method of generating text against a black box for text classification model, such as figure 1 Shown, including S101, acquires the original black box corpus and classification label text classification model; S102, the original word corpus, to obtain the word corresponding to the sequence of the original corpus; S103 respectively acquire the predetermined word sequence number of each word synonyms ; S104 successively replaced by synonyms for each word in the sentence candidate position of the word to form a new sentence to form a new set of candidate text; S105 with the new set of candidate text sentence for each classification sequentially, as the black text box model input, respectively output corresponding to the minimum value of the probability of the selected original label corresponding to the K original corpus of sentences against a set of text, wherein, K is an integer. Described in detail below for each step.
[0047] ...
Embodiment 2
[0062] Example embodiment of the present embodiment, a method of generating text against a black box for text classification model, comprising: a first step, retrieve the original black box corpus and classification label text classification model; the second step, the original word corpus, to obtain the original corpus a corresponding word sequence; a third step of acquiring the sequence of words in each predetermined number of synonyms for each word; a fourth step, the importance of each word in a word sequence is calculated, the importance level of each press sorting words; a fifth step of sequentially replacing the candidate word to the sentence position of each word by a synonym new sentence is formed, to form a new set of candidate text; a sixth step, with the new candidate set for each sentence in the text Examples of the black box sequentially text classification model input, respectively output corresponding to the lowest probability value selected original label correspo...
Embodiment 3
[0070] Example text against a black box for text generation system of the present embodiment classification model, such as figure 2 As shown, including:
[0071] Obtaining module 301, configured to obtain the original black box corpus and classification label text classification model;
[0072] Segmentation module 302, for the original corpus word to obtain the word corresponding to the sequence of the original corpus;
[0073] Synonyms module 303, respectively, for each word in the word sequence acquired a preset number of synonyms;
[0074] Candidate module 304, for sequentially replacing the position of the candidate sentence with a synonym word each word to form a new sentence to form a new set of candidate text;
[0075] Against the text generation module 305, with the new candidate text for each of the set sequentially as input a sentence of the text classification model is a black box, respectively corresponding to the output, the original label selected original corpus cor...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com