Duplicate image detection using deep learning modified SVM and k-NN classification method for multimedia application

This paper discussed an efficient technique to detect near-duplicate images with higher accuracy. The proposed method improved the existing accuracy of near-duplicate image detection and classification using Haar wavelet feature extraction and classification algorithm. Due to advancements in digital image acquisition and image editing software, near-duplicate image counterfeiting has become predictable. This expanding problem needs the creation of sophisticated detection algorithms capable of effectively preventing the spread of near-duplicate image deception. Support vector machine (SVM) and K-near neighbor (KNN) classifiers are used in the classification stage of a modified deep learning model with a convolutional neural network (CNN). The classification model finds the better result on the Imperial College London dataset, with 99.12% using CNN with KNN classifier and 98.63% for CNN with SVM classifier. The classification model also gives better results on the Nanyang Technological University dataset, with 98.36% using CNN with the KNN classifier and 97.85% for CNN with the SVM classifier. The model is tested using publicly available images to find the results. According to the comparison results, this model performed significantly better result than the previous model. As a result, digital image forensics has become more critical, particularly in identifying image duplication.