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Review of Image Forensic Techniques Based on Deep Learning

Author

Listed:
  • Chunyin Shi

    (School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
    These authors contributed equally to this work.)

  • Luan Chen

    (School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
    These authors contributed equally to this work.)

  • Chengyou Wang

    (School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
    Shandong University–Weihai Research Institute of Industrial Technology, Weihai 264209, China)

  • Xiao Zhou

    (School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
    Shandong University–Weihai Research Institute of Industrial Technology, Weihai 264209, China)

  • Zhiliang Qin

    (School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
    Weihai Beiyang Electric Group Co., Ltd., Weihai 264209, China)

Abstract

Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields.

Suggested Citation

  • Chunyin Shi & Luan Chen & Chengyou Wang & Xiao Zhou & Zhiliang Qin, 2023. "Review of Image Forensic Techniques Based on Deep Learning," Mathematics, MDPI, vol. 11(14), pages 1-33, July.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3134-:d:1195122
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    References listed on IDEAS

    as
    1. Rui-Qiang Ma & Xing-Run Shen & Shan-Jun Zhang, 2020. "Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-8, November.
    2. Pingping Zeng & Lianhui Tong & Yaru Liang & Nanrun Zhou & Jianhua Wu, 2022. "Multitask Image Splicing Tampering Detection Based on Attention Mechanism," Mathematics, MDPI, vol. 10(20), pages 1-13, October.
    3. Yuqing Zhao & Guangyuan Fu & Hongqiao Wang & Shaolei Zhang, 2020. "The Fusion of Unmatched Infrared and Visible Images Based on Generative Adversarial Networks," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, March.
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