Out-of-context misinformation detection method based on stance analysis
As artificial intelligence-based automatic generation technology advances, the emergence of misinformation in more sophisticated guises has become a significant threat to the economy and social order. Among its forms, out-of-context misinformation stands out as particularly deceptive and readily exe...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2024-04-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024017 |
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| _version_ | 1841529511008534528 |
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| author | YUAN Xin GUO Jie QIU Weidong HUANG Zheng |
| author_facet | YUAN Xin GUO Jie QIU Weidong HUANG Zheng |
| author_sort | YUAN Xin |
| collection | DOAJ |
| description | As artificial intelligence-based automatic generation technology advances, the emergence of misinformation in more sophisticated guises has become a significant threat to the economy and social order. Among its forms, out-of-context misinformation stands out as particularly deceptive and readily executable. This type of misinformation involves malicious actors enhancing the credibility of false narratives by misrepresenting contextual details such as individuals, events, and locations within real images. To address the shortcomings of current detection algorithms, which heavily depend on knowledge bases and often overlook the stance relationship between the information under scrutiny and online evidence, a stance analysis-based out-of-context misinformation detection method was developed. This method involved several steps for the detection of an image-caption pair along with the corresponding textual and visual evidence retrieved from the Internet. Initially, a stance gain score for each piece of textual evidence was calculated based on the co-occurrence of named entities. Subsequently, independent stance analysis networks were utilized to perform hierarchical clustering on both the image and visual evidence, as well as on the caption and textual evidence. This process involved the extraction of semantic stance representations, facilitated by multiple attention mechanisms and a stance analysis module. The authenticity of the image-caption pair was subsequently predicted based on the outcomes of semantic comparison and stance analysis. Experimental results indicate that the incorporation of stance analysis significantly enhances the method's detection capabilities. Specifically, the accuracy of this method outperforms the state-of-the-art algorithm that employs Internet evidence for detection by 2.3%. |
| format | Article |
| id | doaj-art-3fc32fda7dd84f33a4480567d185f7d1 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2024-04-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-3fc32fda7dd84f33a4480567d185f7d12025-01-15T03:17:05ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2024-04-011013314263897221Out-of-context misinformation detection method based on stance analysisYUAN XinGUO JieQIU WeidongHUANG ZhengAs artificial intelligence-based automatic generation technology advances, the emergence of misinformation in more sophisticated guises has become a significant threat to the economy and social order. Among its forms, out-of-context misinformation stands out as particularly deceptive and readily executable. This type of misinformation involves malicious actors enhancing the credibility of false narratives by misrepresenting contextual details such as individuals, events, and locations within real images. To address the shortcomings of current detection algorithms, which heavily depend on knowledge bases and often overlook the stance relationship between the information under scrutiny and online evidence, a stance analysis-based out-of-context misinformation detection method was developed. This method involved several steps for the detection of an image-caption pair along with the corresponding textual and visual evidence retrieved from the Internet. Initially, a stance gain score for each piece of textual evidence was calculated based on the co-occurrence of named entities. Subsequently, independent stance analysis networks were utilized to perform hierarchical clustering on both the image and visual evidence, as well as on the caption and textual evidence. This process involved the extraction of semantic stance representations, facilitated by multiple attention mechanisms and a stance analysis module. The authenticity of the image-caption pair was subsequently predicted based on the outcomes of semantic comparison and stance analysis. Experimental results indicate that the incorporation of stance analysis significantly enhances the method's detection capabilities. Specifically, the accuracy of this method outperforms the state-of-the-art algorithm that employs Internet evidence for detection by 2.3%.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024017out-of-contextstance analysismisinformationinternet evidence |
| spellingShingle | YUAN Xin GUO Jie QIU Weidong HUANG Zheng Out-of-context misinformation detection method based on stance analysis 网络与信息安全学报 out-of-context stance analysis misinformation internet evidence |
| title | Out-of-context misinformation detection method based on stance analysis |
| title_full | Out-of-context misinformation detection method based on stance analysis |
| title_fullStr | Out-of-context misinformation detection method based on stance analysis |
| title_full_unstemmed | Out-of-context misinformation detection method based on stance analysis |
| title_short | Out-of-context misinformation detection method based on stance analysis |
| title_sort | out of context misinformation detection method based on stance analysis |
| topic | out-of-context stance analysis misinformation internet evidence |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024017 |
| work_keys_str_mv | AT yuanxin outofcontextmisinformationdetectionmethodbasedonstanceanalysis AT guojie outofcontextmisinformationdetectionmethodbasedonstanceanalysis AT qiuweidong outofcontextmisinformationdetectionmethodbasedonstanceanalysis AT huangzheng outofcontextmisinformationdetectionmethodbasedonstanceanalysis |