Detection and localization of image tampering in digital images with fused features

Abstract

Summary In digital forensics, image tamper detection and localization have attracted increased attention in recent days, where the standard methods have limited description ability and high computational costs. As a result, this research introduces a novel picture tamper detection and localization model. Feature extraction, tamper detection, as well as tamper localization are the three major phases of the proposed model. From the input digital images, a group of features like “Scale‐based Adaptive Speeded Up Robust Features (SA‐SURF), Discrete Wavelet Transform (DWT) based Patched Local Vector Pattern (LVP) features, HoG feature with harmonic mean based PCA and MBFDF” are extracted. Then, with this extracted feature strain the “optimized Convolutional Neural Network (CNN)” will be trained in the tamper detection phase. Since it is the key decision‐maker about the presence/absence of tamper, its weighting parameters are fine‐tuned via a novel improved Sea‐lion Customized Firefly algorithm (ISCFF) model. This ensures the enhancement of detection accuracy. Once an image is recognized to have tampers, then it is essential to identify the tamper localization. In the tamper localization phase, the copy‐move tampers are localized using the SIFT features, splicing tampers are localized using the DBN and the noise inconsistency is localized with a newly introduced threshold‐based tamper localization technique. The simulation outcomes illustrate that the adopted model attains better tamper detection as well as localization performance over the existing methods.

DS‐Net: Dual supervision neural network for image manipulation localization

“…There are various types of these methods such as keypoint‐based algorithms [4, 5], blockbased algorithms [6, 7], and noise‐based algorithms [8], among others. The rapid development of deep learning has led to fast growth in the field of digital multimedia forensics, where convolutional neural networks (CNNs) are increasingly used instead of or in combination with traditional manual feature design [9–‐11]. In real life, we can identify whether an image is tampered or not and generate an approximate localization of the tampered area by simply feeding the image into the detection algorithms.…”