Understanding digital image anti-forensics: an analytical review.
Saved in:
| Title: | Understanding digital image anti-forensics: an analytical review. |
|---|---|
| Authors: | Taneja, Neeti, Bramhe, Vijendra Singh, Bhardwaj, Dinesh, Taneja, Ashu |
| Source: | Multimedia Tools & Applications; Jan2024, Vol. 83 Issue 4, p10445-10466, 22p |
| Subject Terms: | IDENTITY theft, JPEG (Image coding standard), COMPUTER crimes, FORENSIC sciences, FORGERY |
| Abstract: | Image forensics is essential for detecting image manipulation, authenticating images, and identifying sources of images. A forensic analyst can make use of various artifacts to develop a powerful forensic technique. These artifacts include JPEG blocking and quantization artifacts, streaking artifacts and contrast enhancement artifacts, etc. With the introduction of anti-forensics, it has become difficult for forensic experts to identify forged images. There are various anti-forensic methods available that try to eradicate these detection footprints/artifacts to fool the existing forensic detectors. Thus the detection of anti-forensic attacks is very crucial and plays a vital role in forensic analysis. This paper presents a review of various types of anti-forensic attacks, such as JPEG anti-forensics, Contrast enhancement anti-forensics, and Median filtering anti-forensics. Firstly a brief introduction is given about image forgery, JPEG compression, contrast enhancement, and median filtering. Then, anti-forensics is described in detail, and finally, the recent state-of-the-art anti-forensic techniques are summarized in tabular form for better understanding. This may be helpful for the forensic analyst to develop robust methods for forgery detection that can be applied in various applications such as the identification of cybercrimes, identity thefts, etc. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!