@inproceedings{022ae034e660412da4506040db430810,
title = "Evaluating Perceptual Hashing Algorithms in Detecting Image Manipulation Over Social Media Platforms",
abstract = "Perceptual hash is a fingerprint of features of multimedia content. Compared with crypto hash, perceptual hash shows many advantages when defending against image-based fake news attacks in terms of detecting deliberate image manipulation while still tolerating normal format or resolution changes conducted on user-uploaded images by content-hosting providers such as social media platforms. Previous research into perceptual hash has studied general image manipulation without considering legitimate image transformation by social media platforms. This paper evaluates and analyzes six state-of-the-art perceptual hash algorithms for detecting image manipulation over two major social media platforms: Facebook and Twitter. Our real-world image evaluation shows differences in the two platforms' image processing and how the six algorithms perform in detecting image manipulation over these platforms. We also present a new approach to finding the optimal detection threshold for each perceptual hash algorithm in distinguishing the platform's standard image processing from deliberate image manipulation.",
keywords = "Computer security, Digital forensics, Fake news, Perceptual hashing, Social media",
author = "Mohammed Alkhowaiter and Khalid Almubarak and Cliff Zou",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022 ; Conference date: 27-07-2022 Through 29-07-2022",
year = "2022",
doi = "10.1109/CSR54599.2022.9850288",
language = "English",
series = "Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "149--156",
booktitle = "Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022",
address = "United States",
}