DeepMark: A Platform Ensuring Content Integrity

Social media content integrity evaluation is necessary to avoid unauthorized redistribution and alteration of multimedia content. The traditional approaches are primarily based on metadata or watermarking methods, which are easily tampered with or deleted, and thus are insufficient to guarantee long-term content authenticity. This renders it difficult to maintain ownership authentication and prevent content tampering. To counter these weaknesses, we suggest an improved system that employs DWT-based watermarking, facial feature hashing, and cryptographic metadata embedding to ensure content integrity. The system delves into the spatial coordinates of facial landmarks to generate unique hash values that are securely stored in a database to enable verification. The system, through the integration of multi-layer security techniques, manages to eliminate the risk of content re-posting and monitors unauthorized tampering, even when it is covert. Moreover, our method minimizes computational overhead while still being able to verify in real-time, thereby making the system scalable to mass use. The suggested architecture enhances the security and reliability of user-generated media on social networks, offering an end-to-end solution for preventing media forgery and abuse.