A Bit-Plane Slicing Technique for the Classification of Anti-forensically Contrast-Enhanced Images

Digital images are frequently employed in many sectors, including forensic investigations, medical CT, handwriting recognition, and computer-aided diagnosis. These images are easily modifiable by fraudsters using various image editing techniques. This means that they must be kept safe since they contain a wealth of important information. Many forgery detection techniques exist, but anti-forensics acts as a barrier to these techniques in detecting forgery in a given image. Contrast enhancement operation is generally employed for blending two different regions while creating a forgery. A forgery detection technique can exploit contrast enhancement artifacts to identify the presence of a forged area. However, anti-forensics can enhance a given image without introducing contrast enhancement artifacts, which makes it difficult for forensic analysts to detect forgery. In the current digital era, identifying these enhancement processes is crucial for obtaining genuine data. This paper presented an efficient technique for classifying original, contrast-enhanced, and anti-forensically contrast-enhanced images. The analytical results show how well the suggested strategy works and attains good accuracy.