A Tale of a Deep Learning Approach to Image Forgery Detection

2018 5th International Conference on Networking, Systems and Security (NSysS)(2018)

Cited 2|Views7
No score
Social media has become a part and parcel in people’s lives in modern days. Due to the high availability of cameras and presence of various image editing tools in mobiles, it has become much easier to forge fake images. Such false and fake images can and are being used for slandering reputation of popular figures and in dirty political moves. The problem of detecting and localizing such images is known as image forgery detection. Due to how small the edited regions are compared to the full image size, deep learning without manual feature engineering approaches are quite difficult to apply. In this paper, we focus on binary classification of forged or authentic images, and we highlight how deep learning models should be leveraged for good performance in forged image classifications.
Translated text
Key words
Forgery,Feature extraction,Deep learning,Splicing,Discrete cosine transforms,Media,Signal processing
AI Read Science
Must-Reading Tree
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined