View article

As digital image forgery can be alarmingly detrimental, therefore, an insight into detection and classification of tampered digital images is of paramount importance. Without undermining the significance of other image forgery types, copy-move can be regarded as one of the most commonly used forgeries due to its ease of implementation. To counter the rapidly complicating forgery methods due to easily accessible technologically advanced tools, passive image forensic methods have also undergone massive evolution. Presently, deep learning based techniques are regarded as state-of-the-art for image processing/image forgery detection and classification due to their enhanced accuracy and automatic feature extraction capabilities. But the existing deep learning based techniques are time and resource-intensive as well. To cater for these solutions with complexities as stated, this research focuses on …