Tampering Detection and Segmentation Model for Multimedia Forensic

Abstract

When an image undergoes hybrid post-processing transformation, detecting tamper region, localizing it and segmentation becomes very difficult tasks. In particular, when a copy-move attack with hybrid transformation has similar contrast and illumination parameters with an authenticated image it makes tamper detection difficult. Alongside, under small-smooth attack existing tamper identification model provides a very poor segmentation outcome and sometimes fails to identify an image as tampered. This article focused on addressing the difficulty through the adoption of the Deep Learning model. The proposed technique is efficient in detecting tampering with good segmentation outcomes. However, existing models fail to distinguish adjacent pixels' relationships affecting segmentation outcomes. In this paper, an Improved Convolution Neural Network (ICNN) assuring correlation awareness-based Tamper Detection and Segmentation (TDS) model for image forensics is presented. This model brings good correlation among adjacent pixels through the introduction of an additional layer namely the correlation layer alongside vertical and horizontal layers. The TDS-ICNN is very effective in localizing and segmenting tamper regions even under small-smooth postprocessing tampering attacks by using a feature descriptor built using aggregated three-layer ICNN architecture. An experiment is done to study TDS-ICNN with other tamper identification models using various datasets such as MICC, Coverage, and CoMoFoD. The TDS-ICNN is very efficient under different postprocessing hybrid attacks when compared with existing models.