Securing the Socio-Cyber World
Multiorder Attribute Node Association Classification for Manipulated Media
Bibliographic Data
| ID | 22107154 |
|---|---|
| Authors | Shuai Xiao (0000-0003-4058-8120, Tianjin University), Guipeng Lan (0000-0001-7321-7460, Tianjin University), Jiachen Yang (0000-0002-3090-2465, Tianjin University), Yang Li (0000-0002-2835-6661, Shihezi University), Jiabao Wen (0009-0002-7631-7701, Tianjin University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 4 |
| Pages | 4809-4818 |
| Publication date | 2024-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3213832 |
| OpenAlex | W4312785994 |
| Language | EN |
| Citations received | 2 |
| References cited | 35 |
With the rapid development of information technology, social network has become an indispensable part of daily life. People have been able to get news from all over the world through social networks for a long time. People spend more time online than they do in real life. However, the information we get in the world of social network is not purely benign. Due to the development of artificial intelligence technology, more and more tampered media information appears in social networks, some for entertainment, while others become the dark side of social networks, of which the most harmful is to people in the media tamper. For fake news and misinformation caused by media tampering, we need to trace the source and clearly distinguish the truth from the manipulated. This article proposes an image media forgery classification method of multiorder attribute nodes. First, we use different methods to extract the edge, texture, grayscale, and color attributes of the image. Second, according to the characteristics of different attributes, we calculate the first-order entropy of edge attributes, the second-order entropy of texture attributes, local entropy of grayscale, and color properties. Finally, we represent each image with some nodes and build a graph convolutional network (GCN) to classify real and fake images. Experimental results on mainstream media manipulation datasets show that our method is the state-of-the-art compared with similar methods
Computer vision · Convolutional neural network · Grayscale · Social media · World Wide Web · Advanced Malware Detection Techniques · Computer Science · Digital Media Forensic Detection · Misinformation and Its Impacts · Artificial Intelligence
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| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |