@article{4572,
  author = {Sonal Lakade, Shyam Khairkar, Praveen Kokane, Amol Kale, Rajivkumar Mente},
  title = {Deep Learning based Detection of AI generated Synthetic Images for Digital Forensics},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2025},
  volume = {16},
  number = {3},
  doi = {https://doi.org/10.6025/jmpt/2025/16/3/113-137},
  url = {https://www.dline.info/jmpt/fulltext/v16n3/jmptv16n3_1.pdf},
  abstract = {The rapid advancement of developments in artificial intelligence (AI), particularly in Generative Adversarial
Networks (GANs), has paved the way for the creation of extremely realistic synthetic images that pose a
challenge for digital forensics. Traditional image authentication techniques lack the pace to catch up with
the growing sophistication of AI-synthesised images, calling for more innovative detection methods. This
review examines the potential application of deep learning technologies, specifically Convolutional Neural
Networks (CNNs), in detecting AI-generated synthetic images. The paper discusses several conventional
and deep learning based approaches, compares their performance, and indicates the significance of lightweight
CNN models in maintaining computational efficiency without sacrificing accuracy. In addition, it
elaborates on the implications of explainable AI in bringing transparency to detection models. The review
also explores the importance of synthetic image data in computer vision tasks, as well as its challenges,
including domain gaps and biases. Lastly, the application of digital forensics in preventing misinformation
and malicious acts involving synthetic images is discussed. The results underscore the importance of reliable
and interpretable deep learning based detection methods in preserving the integrity of digital forensic examinations.},
}