Research about the Ability of LLM in the Tamper-Detection Area
Abstract: In recent years, particularly since the early 2020s, LLMs have emerged as the most powerful AI tools in addressing a diverse range of challenges, from natural language processing to complex problem-solving in various domains. In the field of tamper detection, LLMs are capable of identifying basic tampering activities.To assess the capabilities of LLMs in more specialized domains, we have collected five different LLMs developed by various companies: GPT-4, LLaMA, Bard, ERNIE Bot 4.0, and Tongyi Qianwen. This diverse range of models allows for a comprehensive evaluation of their performance in detecting sophisticated tampering instances.We devised two domains of detection: AI-Generated Content (AIGC) detection and manipulation detection. AIGC detection aims to test the ability to distinguish whether an image is real or AI-generated. Manipulation detection, on the other hand, focuses on identifying tampered images. According to our experiments, most LLMs can identify composite pictures that are inconsistent with logic, and only more powerful LLMs can distinguish logical, but visible signs of tampering to the human eye. All of the LLMs can't identify carefully forged images and very realistic images generated by AI. In the area of tamper detection, LLMs still have a long way to go, particularly in reliably identifying highly sophisticated forgeries and AI-generated images that closely mimic reality.
- Y. Chang, X. Wang, J. Wang, Y. Wu, K. Zhu, H. Chen, L. Yang, X. Yi, C. Wang, Y. Wang et al., “A survey on evaluation of large language models,” arXiv preprint arXiv:2307.03109, 2023.
- H. Naveed, A. U. Khan, S. Qiu, M. Saqib, S. Anwar, M. Usman, N. Barnes, and A. Mian, “A comprehensive overview of large language models,” arXiv preprint arXiv:2307.06435, 2023.
- J. Fridrich, “Methods for tamper detection in digital images,” in Multimedia and Security, Workshop at ACM Multimedia, vol. 99, 1999, pp. 29–34.
- M. Mishra and F. Adhikary, “Digital image tamper detection techniques-a comprehensive study,” arXiv preprint arXiv:1306.6737, 2013.
- H. Guan, M. Kozak, E. Robertson, Y. Lee, A. N. Yates, A. Delgado, D. Zhou, T. Kheyrkhah, J. Smith, and J. Fiscus, “Mfc datasets: Large-scale benchmark datasets for media forensic challenge evaluation,” in 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). IEEE, 2019, pp. 63–72.
- T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4396–4405.
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