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FixProof — AI-Powered Media Verification & Evidence Intelligence The Problem: Investigations Haven't Kept Up With Content Distribution Digital content can be copied, edited, and redistributed in seconds. Yet identifying unauthorized reuse and verifying suspicious media remains a largely manual and time-consuming process. For sports broadcasters, media-rights holders, and content investigators, a suspected case of unauthorized content reuse can involve: - Manually searching through large volumes of video footage. - Comparing frames and identifying visually similar scenes. - Collecting supporting evidence from different sources. - Interpreting whether potential matches are meaningful. - Preparing investigation reports and organizing evidence. Traditional workflows rely heavily on human effort, fragmented tools, and basic similarity checks. These methods may identify visual resemblance, but they often fail to provide the context and structured evidence needed for a meaningful investigation. The Solution: Turning Manual Investigation into an AI-Assisted Workflow FixProof transforms media verification from a manual search task into a connected, AI-assisted investigation process. It combines computer vision, visual similarity retrieval, and multimodal AI to help investigators move from suspicious footage to structured, reviewable evidence. How FixProof Works 1. Media Ingestion & Processing** - Ingests video footage for investigation. - Extracts representative visual keyframes for analysis. 2. Computer Vision & Similarity Detection** - Uses MobileNetV3 to generate visual embeddings. - Uses FAISS for efficient retrieval of visually similar content. - Applies perceptual hashing and additional visual signals to help narrow down potential matches. 3. AI-Powered Investigation with Meta Muse** Finding visually similar frames is only the beginning. A similarity score alone cannot explain why two pieces of footage may be related, what visual evidence supports a match, or what an investigator should examine next. This is where Meta's Muse models become the intelligence layer of FixProof.** - Muse Vision: Helps examine shortlisted visual evidence and interpret potential matches. - Muse Reasoning: Helps transform comparison results into contextual, structured investigative findings. - Segment Anything Model (SAM): Supports visual localization workflows, helping investigators examine relevant regions of visual evidence. 4. Evidence Organization & Reporting - Brings together potential matches and AI-assisted investigative findings. - Organizes results into a structured evidence dossier. - Helps investigators review and communicate their findings through a unified workflow. Why Meta's Models Matter FixProof does not use AI simply to generate text. Meta's models contribute to the investigation itself. Computer vision discovers potential matches, while Muse-powered multimodal analysis helps interpret the selected evidence and turn raw comparison results into meaningful investigative insights. This creates a workflow in which visual retrieval and AI reasoning complement each other rather than operating as disconnected tools. Initial Application & Vision FixProof is initially focused on sports broadcasting and media-rights investigations, where the volume and speed of content distribution make manual verification particularly challenging. The underlying approach also has potential applications across other media-verification workflows. We are not building another AI chatbot. We are building an AI-assisted investigation system — where computer vision discovers, Muse reasons, and evidence brings everything together. With human review retained for consequential decisions, FixProof aims to make media investigations faster, more systematic, and easier to verify.