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Ever since human society entered the age of social media, every user has had a considerable amount of visual content stored online and shared in variant virtual communities. As an efficient information circulation measure, disastrous consequences are possible if the contents of images are tampered with by malicious actors. Specifically, we are witnessing the rapid development of machine learning (ML) based tools like DeepFake apps. They are capable of exploiting images on social media platforms to mimic a potential victim without their knowledge or consent. These content manipulation attacks can lead to the rapid spread of misinformation that may not only mislead friends or family members but also has the potential to cause chaos in public domains. Therefore, robust image authentication is critical to detect and filter off manipulated images. In this paper, we introduce a system that accurately AUthenticates SOcial MEdia images (AUSOME) uploaded to online platforms leveraging spectral analysis and ML. Images from DALL-E 2 are compared with genuine images from the Stanford image dataset. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used to perform a spectral comparison. Additionally, based on the differences in their frequency response, an ML model is proposed to classify social media images as genuine or AI-generated. Using real-world scenarios, the AUSOME system is evaluated on its detection accuracy. The experimental results are encouraging and they verified the potential of the AUSOME scheme in social media image authentications. 

The information era has gained a lot of traction due to the abundant digital media contents through technological broadcasting resources. Among the information providers, the social media platform has remained a popular platform for the widespread reach of digital content. Along with accessibility and reach, social media platforms are also a huge venue for spreading misinformation since the data is not curated by trusted authorities. With many malicious participants involved, artificially generated media or strategically altered content could potentially result in affecting the integrity of targeted organizations. Popular content generation tools like DeepFake have allowed perpetrators to create realistic media content by manipulating the targeted subject with a fake identity or actions. Media metadata like time and location-based information are altered to create a false perception of real events. In this work, we propose a Decentralized Electrical Network Frequency (ENF)-based Media Authentication (DEMA) system to verify the metadata information and the digital multimedia integrity. Leveraging the environmental ENF fingerprint captured by digital media recorders, altered media content is detected by exploiting the ENF consistency based on its time and location of recording along with its spatial consistency throughout the captured frames. A decentralized and hierarchical ENF map is created as a reference database for time and location verification. For digital media uploaded to a broadcasting service, the proposed DEMA system correlates the underlying ENF fingerprint with the stored ENF map to authenticate the media metadata. With the media metadata intact, the embedded ENF in the recording is compared with a reference ENF based on the time of recording, and a correlation-based metric is used to evaluate the media authenticity. In case of missing metadata, the frames are divided spatially to compare the ENF consistency throughout the recording.