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 several deepfake detector algorithms and forensic tools into one unified application was complex, requiring careful optimization to maintain accuracy without slowing performance.
The software needed to analyze high-resolution deepfake videos in real time without compromising speed or accuracy.
Providing instant progress updates during analysis was critical but difficult to achieve without interrupting the deepfake detection technology’s processing pipeline.
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
Built a backend system that runs multiple deepfake detectors and analyzers in parallel, combining forensic tools for higher accuracy and faster analysis.
Optimized File Handling for Large Media
Implemented efficient streaming and compression techniques that let the deepfake analyzer process high-resolution videos and images smoothly, with no performance trade-offs.
Real-Time Deepfake Analysis Dashboard
Integrated WebSocket communication so users see live detection progress and deepfake classification updates instantly, improving trust and usability.
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 analyzers in parallel to achieve high accuracy across formats and resolutions.
Processes high-resolution videos and gigabyte-scale files without any delay, production-readying the deepfake detection tool for real-world production loads.
Utilizes WebSocket communication for real-time deepfake video detection results with instant feedback.
Detects and classifies deepfake videos in real time, giving investigators confidence scores and visual indicators.
Delivers easy-to-read, straightforward reports with timestamps, visual indicators, and detection accuracy scores to allow media teams and law enforcement to act promptly.
Utilizes compression and memory-efficient streaming for large file handling without quality loss, allowing scalable and stable deepfake detection.
We created a production-level, cross-platform application capable of quickly and effectively detecting deepfakes, prioritizing performance and real-time feedback.
Successfully incorporated various deepfake detection technologies with a detection accuracy of over 92% in live testing on varied datasets.
Handled media files of gigabyte size in under three minutes without crashing, demonstrating scalable deepfake analyzer performance.
Boosted detection reliability using complementary forensic methods, reducing false positives and false negatives by 25%.
Delivered real-time detection updates and immediate results, improving user satisfaction and cutting wait times by over 40%.
This deepfake detection software processes HD videos in under 3 minutes with 92% accuracy, helping media companies, law enforcement, and researchers identify deepfake content in real time.
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