Image Authentication Using Self-Supervised Learning to Detect Manipulation Over Social Network Platforms

Social media nowadays has a direct impact on people's daily lives as many edge devices are available at our disposal and controlled by our fingertips. With such advancement in communication technology comes a rapid increase of disinformation in many kinds and shapes; faked images are one of the primary examples of misinformation media that can affect many users. Such activity can severely impact public behavior, attitude, and belief or sway the viewers' perception in any malicious or benign direction. Mitigating such disinformation over the Internet is becoming an issue with increasing interest from many aspects of our society, and effective authentication for detecting manipulated images has become extremely important. Perceptual hashing (pHash) is one of the effective techniques for detecting image manipulations. This paper develops a new and a robust pHash authentication approach to detect fake imagery on social media networks, choosing Facebook and Twitter as case studies. Our proposed pHash utilizes a self-supervised learning framework and contrastive loss. In addition, we develop a fake image sample generator in the pre-processing stage to cover the three most known image attacks (copy-move, splicing, and removal). The proposed authentication technique outperforms state-of-the-art pHash methods based on the SMPI dataset and other similar datasets that target one or more image attacks types.