Performance Analysis of Image Forgery Detection using Transform Function and Machine Learning Algorithms

Abstract

The image forgery process deformed the reputation of digital multimedia data in the era of internet technology. The availability of image editing software promotes the activity of image forgery. The action of forgery rise Ransome demands from the legitimate use of social media. The increasing rate of multimedia temptation changes the authenticity of data. Image forgery detection is a vital challenge for research scholars in the current decade. The forgery detection detects the location of forgery in the image and ensures the authentication of the picture. This paper represents the study of image forgery detection based on transform methods and machine learning. The transform methods have been great potential in image processing and pattern recognition. The various derivates of transform methods estimate the image forgery. The variants of transform include discrete wavelet transform, DCT, FFT, SIFT and many more transform. The applied transform methods have certain limitations and the detection of forged image compromised. The machine learning algorithm increases the detection ratio of image forgery. The trends of machine learning algorithms focus on image forgery detection and improve the detection ratio. Machine learning provides various classification and clustering algorithms for image forgery detection. This paper analyzed the experimental performance of image forgery detection based on transform and machine learning algorithms. The analysis process uses MATLAB tools and a standard image forgery dataset for the detection ratio. The analysis of results suggests that the machine learning algorithm is very efficient instead of transform-based methods.