Image Forgery Detection using AKAZE Keypoint Feature Extraction and Trie Matching

Abstract

Image Forgery is an illegal activity in the society as per cyber laws. There are various types of forgeries in which forgery on images is considered as an illegal activity. Image forgery may take place in different ways. One way for doing forgery on images is copy and move forgery which may result in loss of image integrity or authenticity. There are number of popular detection techniques exist such as SIFT, SURF etc., but have high complexity in detection of forgery. Here we have proposed a method to detect the forgery on images which results in loss of integrity or authenticity. In our proposed method we have used descriptor matching using Trie Data Structure The descriptor matching method of implementation using Trie data structure made the complexity of the problem to reduce to O (n log n). Using Key points approach we can verify the integrity of the image. Extracting the features with key points approach is computational expensive task. But there is KAZE method which overcomes this situation. KAZE’s method of using non-linear diffusion filtering requires it to solve a series of PDEs. This cannot be done analytically forcing KAZE to use a numerical method called an AOS scheme to solve the PDEs. However, this process is computationally costly and therefore an accelerated version of KAZE was created. The Accelerated KAZE or AKAZE which creates non-linear scale space through Fast Explicit Diffusion for reduce the complexity in extracting the features.

Real-time currency recognition on video using AKAZE algorithm

“…The next step is creating a descriptor [17]. AKAZE algorithm generates a descriptor on each keypoint that scales and rotates invariant.…”