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Survey De-identification Generative Adversarial Network Based | ||
| Al-Noor Journal for Information Technology and Cybersecurity | ||
| Volume 3, Issue 1, June 2026, Pages 104-111 PDF (544.74 K) | ||
| Document Type: Original Article | ||
| DOI: 10.69513/jncs.v3.i1.a9 | ||
| Authors | ||
| Marwan Khaleel Majeed AL-Ali1; Saif Saaduldeen Ahmed2; S M Abdul Mueid3; Md Zayed Al Sajed4 | ||
| 1Department of Applied Artificial Intelligence, Artificial Intelligence Research Center, Northern Technical University, Mosul, Iraq | ||
| 2Department of Information Technology, Al-Imam Al- A’dham University, College Mosul, Iraq. | ||
| 3Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, 86400, Malaysia | ||
| 4TerraXyn Research, Canada | ||
| Abstract | ||
| There are several uses for the Generative Adver- sarial Network (GAN) technology. Anonymity, personal privacy, and protections for officials, managers, and powerful people are important legal concerns. These significant advancements, including facial recognition, are led by GAN-based technologies. This paper compares several Generative Adversarial Network types used in the de-identification field, depending on state of the art, such as privacy protection Generative Adversarial Net- work (PPGAN), conditional identity anonymization Generative Adversarial Network (CIAGAN), and semantic aware Genera- tive Adversarial Network (SAGAN), among others, to high-end products presented by researchers through multiple databases, including Celeb Face Attributes (CelebA), among others, to obtain the most accurate expressions and characteristics of real face images. Researchers used a range of techniques and strategies to present their findings and compare them to previous findings to obtain the best responses for de-identification. The strengths and weaknesses of developing new faces depend on the additions made to each proposed structure and the exploitation of raw resources into the basic system, which is reliant on the network’s structure. After discussing each technique and the relevant technique for assessing the output—such as Siamese for true/face verification and Learned Perceptual Image Patch Similarity (LPIPS)—they were grouped in a table with the other techniques to clarify the differences. This research has produced several insightful findings, such as increased interest in the subject of identity concealment and developments in GAN technology, which provoked scholarly discussion. | ||
| Keywords | ||
| Terms— Generative Adversarial Network , De- identification , Computer Vision , Deep Neural Network | ||
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