ABSTRACT
Image Forgery is a common practice that can be observed in many of the social networking platforms as memes, animations, fake news, trolls and others. Now days, people in social media platforms got vexed with these fake news because these land them in confusion state. The latest case study in this pandemic is associated with viral news about COVID-19 variants, lockdowns, and vaccinations, which created a lot of tensions among the public. Traditional image processing techniques like PCA, LBP, RBF, and others are popular techniques to identify the forgery images but most of them are unsuccessful while dealing with high dimensionality, noisy, blur images. The people using social network sites need an "Efficient Identification of Copy Move Forgery Detection Techniques” to recognize the fake news. The existing approaches find the overlapped regions to identify the tampered parts in the images but the deep learning mechanisms tries to identify the non-overlapping regions and location parameter optimizations. In this paper, the focus is on various approaches available in the current scenario to detect the forgery parts in the image. © 2022, Mathematical and Research Society. All rights reserved.
ABSTRACT
Image Forgery is a common practice that can be observed in many of the social networking platforms as memes, animations, fake news, trolls and others. Now days, people in social media platforms got vexed with these fake news because these land them in confusion state. The latest case study in this pandemic is associated with viral news about COVID-19 variants, lockdowns, and vaccinations, which created a lot of tensions among the public. Traditional image processing techniques like PCA, LBP, RBF, and others are popular techniques to identify the forgery images but most of them are unsuccessful while dealing with high dimensionality, noisy, blur images. The people using social network sites need an “Efficient Identification of Copy Move Forgery Detection Techniques” to recognize the fake news. The existing approaches find the overlapped regions to identify the tampered parts in the images but the deep learning mechanisms tries to identify the non-overlapping regions and location parameter optimizations. In this paper, the focus is on various approaches available in the current scenario to detect the forgery parts in the image. © 2022, Mathematical and Research Society. All rights reserved.