Image-based Ransomware Detection for Social Media Post Using Structural Similarity Index

The rapid advancement of digital technologies allowed people to interact with different social media platforms for communicating and sharing their feeds with their friends and communities. However, sharing and posting on social media are imperiled by various privacy-related issues, such as tampering with the uploaded images. Malicious individuals use manipulated images to exploit users and spread false information. Motivated by the aforesaid issue in this paper, we proposed a framework that can effectively detect manipulated images on social media platforms, specifically tackling image-based ransomware attacks. The proposed framework combines several image processing algorithms, including the Structural Similarity Index, Correlation Coefficient, Image Histogram Comparison, and Image Feature Comparison algorithms, to detect the manipulated images. Through experimentation and performance analysis, it is determined that the proposed framework has varying sensitivity levels to induced noise and manipulation. The results demonstrated the effectiveness of the proposed framework in detecting manipulated images, with the SSIM achieving the highest accuracy and the histogram comparison algorithm being the fastest.