A Deep Learning and Machine Learning Approach for Image Classification of Tempered Images in Digital Forensic Analysis

Multimedia images are the primary source of communication across social media and other websites. Multimedia security has gained the attention of modern researchers and has posed dynamic challenges such as image forensics, image tampering, and deep fakes. Malicious users tamper with the image embedding noise, leading to misinterpretation of the content. Identifying and authenticating the image by detecting the forgery operations performed on it is essential. In our proposed model, we detect the forged region using the machine learning model SVM in the first iteration and Convolution Neural Network in the second iteration with Discrete Cosine Transform (DCT) for feature extraction. The proposed model is tested with a Corel 10K dataset, and an average accuracy of 98% is obtained for all kinds of image operations, including scaling, rotation, and augmentation.

@article{Chitti2022,
  title     = {A Deep Learning and Machine Learning Approach for Image Classification of Tempered Images in Digital Forensic Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {10},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Praveen Chitti and K. Prabhushetty and Shridhar Allagi},
  doi       = {10.14569/IJACSA.2022.0131069},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131069}
}