Project Overview
There were a number of significant challenges for the project, including integrating different deepfake detection algorithms into a single system, processing large high-resolution media files without compromising performance, and issuing real-time progress updates without hindering the analysis.
Combining various deepfake detector algorithms and forensic techniques into one integrated application.
Identify deepfake video in real time, handling high-resolution videos and images without performance issues.
Providing instant updates during ongoing analysis without delaying the process.
Our team developed a solid desktop program that can quickly identify deepfake content, analyzing video and image deepfakes with precision. It processes large media files in an efficient manner, updates progress in real time, and generates compact, simple-to-grasp reports. The software can be executed on Windows, macOS, and Linux and is useful for media companies, law enforcement agencies, and researchers.
Unified Backend Processing Engine
Developed a backend system capable of running multiple deepfake detection models and forensic checks in parallel for faster results.
Optimized File Handling
Implemented efficient file streaming and compression methods to process large media files without excessive memory usage.
Live Data Sync Between Backend and UI
Used WebSocket communication to update the interface in real-time as the backend progressed with analysis.
The Solicy team exceeded our expectations. They not only met the technical requirements but also designed the application to be intuitive and lightning-fast, even for very large files. That they were able to place state-of-the-art deepfake detection models in a stable, user-friendly product is a breakthrough for our digital media verification efforts.
The software combines cutting-edge detection models, forensic analysis, and a cross-platform interface to deliver accuracy, speed, and usability.
Runs multiple deepfake detection models and forensic analyses in parallel to improve accuracy.
Resolves high-resolution big images and videos without any performance loss.
Utilizes WebSocket-based live communication to display live continuous analysis output in real time.
Identifies deepfake video in real time and classifies the content accordingly.
Provides clear-to-read forensic reports with detection confidence scores, timestamps, and visual cues.
Streams and compresses media for the best memory utilization without degrading quality.
We created a production-level, cross-platform application capable of quickly and effectively detecting deepfakes, prioritizing performance and real-time feedback.
Integrated several detection and forensic tools into one seamless backend engine.
Successfully processed gigabyte-sized media files without crashing or slowing down.
Enhanced precision rate by utilizing complementary detection methods.
Real-time progress feedback and results are made available to users without the need to wait for complete analysis completion.
The deepfake detection desktop application we developed can process high-definition media files within less than 3 minutes with more than 92% accuracy of detection for the use of media houses, law enforcement, and research institutions.
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