Identification of image provenance is one of the crucial tasks to be tackled in the field of image forensics. Due to the rapid growth in technology, a huge amount of digital images are shared among users through social networks which facilitates many malicious activities. Establishing the provenance of downloaded images is an important task that could help focus investigations in a specific direction. Such a task is based on the identification of unique fingerprints that are imprinted on images by the social network during the process of upload and download. Based on this, we propose a deep learning based approach that learns the unique traces from the images transformed to the discrete cosine and wavelet domains, to investigate whether the image under test originates directly from a camera or from a specific social network site. The proposed method is able to efficiently identify the specific social network of provenance of the downloaded images and outperforms the state-of-the-art techniques. The encouraging results on images represented in the wavelet domain obtained for the well-known datasets, namely, VISION, IPLAB, and FODB sheds the light into a new way of identifying the image provenance.
|Number of pages||9|
|Journal||Pattern Recognition Letters|
|Publication status||Published - 10-2021|
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence