Unmasking anti-forensic techniques: A DCNN-driven approach to uncover contrast enhancement and median filtering detection.

Abstract

A forensic analyst must utilize a variety of artifacts in order to create a potent forensic method. By eliminating these artifacts, anti-forensic approaches seek to elude forensic detectors. The field of digital image forensics has many difficulties due to the growing sophistication of anti-forensic tactics. Two popular techniques for modifying image characteristics are contrast enhancement and median filtering, which are frequently used to hide signs of manipulation. Therefore, a solution for identifying anti-forensic techniques is urgently needed. This paper presents a multi-class forensic Deep Convolutional Neural Network (DCNN) architecture that combines domain-specific feature streams and residual-domain pre-processing. This pre-processing is designed to reduce image content and highlight manipulation artifacts in order to detect and classify various kinds of image alterations. The DCNN is made to recognize and extract minute manipulation artifacts that are hidden in pixel-level patterns and invisible to the naked eye. The Boss Base dataset is used for training and testing. Experimental assessments show that the proposed model can recognize images that have been exposed to median filtering and contrast enhancement anti-forensics with a good accuracy of 96.42%, even with different levels of manipulation intensity. The proposed model integrates intelligent pre-processing with domain-tailored streams, which makes it robust against compression and is capable of distinguishing between a wide range of complex manipulation types. This strategy fulfills the increasing demand for automated and precise detection techniques in the fight against anti-forensic activities by offering a reliable tool to digital forensic investigators.