ARTICLE AD BOX
I am developing a system that integrates "Source Verifiable" and "Content Decipherable" into a single service workflow. The system uses Robust Watermarking to ensure source authenticity and Tamper Detection (IID-Net) to pinpoint the exact location and scope of any modifications, providing clear visualization evidence.
1) Design Principles and Decision-Making: The system uses a dual-track approach:
Global Traceability (END): Capable of reading and verifying source ownership even after common transmissions like re-compression, scaling, cropping, or minor geometric distortions.
Regional Localization (IID-Net): Outputs pixel-level masks and difference summaries for local manipulations such as splicing, smudging, or replacement.
2) Risk Model and Protection Strategy: In real-world scenarios like "Image Laundering," single-path protection often fails. By combining Robust Traceability with Regional Localization, our system fills the gap where local changes are made without altering the global structure. This is particularly relevant for news fact-checking, platform governance, and judicial evidence.
Question: What are the best practices for implementing such a dual-track inference pipeline in a Flask environment to minimize latency for high-resolution images?
