Latest news with #FACIA
Barnama
23-07-2025
- Business
- Barnama
FACIA Achieves 100 Pct Accuracy On Deepfake Detection Benchmark
BUSINESS KUALA LUMPUR, July 23 (Bernama) -- FACIA, a global leader in facial biometric technology, has announced that its deepfake detection system achieved 100 per cent accuracy on Meta's Deepfake Detection Challenge Dataset (DFDC), reinforcing its position in combatting synthetic media threats. In a statement, the company said its proprietary algorithm was tested on over 100,000 images and videos across multiple datasets, with an overall detection accuracy of 99.6 per cent. 'This is not just about setting a benchmark. Deepfakes are proliferating rapidly, and scalable detection systems are now critical for public agencies, social platforms, and financial services,' said FACIA Chief Technology Officer, Daniyal Assad Chughtai. Deepfake content tripled in 2023, with manipulated media widely used in fraud, disinformation, and non-consensual content, prompting increased concern and regulatory action globally. The DFDC dataset, considered a benchmark for deepfake testing, includes 2,100 altered videos using eight facial modification techniques. FACIA also tested its algorithm on an internal dataset of 3,430 artificial intelligence-generated images from tools such as Midjourney, Artbreeder, and achieving 89.01 per cent accuracy. Combined testing across four additional open-source datasets contributed to the system's total detection score, which FACIA said was relevant to industries such as finance, defence, and immigration. Unlike conventional frame-by-frame methods, FACIA's detection uses a multi-layered pipeline tailored for today's advanced deepfake techniques, with consistently low false acceptance and rejection rates. The technology's reliability is suited to high-assurance identity verification, offering real-time, scalable implementation for deepfake-vulnerable environments. FACIA plans to further enhance its system with multilingual spoof detection, broader dataset training, and improved application programming interface (API) support to facilitate integration with third-party platforms. The announcement comes amid growing scrutiny of synthetic media on major platforms such as Meta, TikTok, and X (formerly Twitter), which are under pressure to counter misinformation and manipulated content.
Barnama
23-07-2025
- Barnama
FACIA Reports 100% Accuracy in Deepfake Detection Across Industry Datasets
LONDON, July 23 (Bernama) -- FACIA, a global leader in facial biometric technology, announced that its deepfake detection system has achieved 100% accuracy on multiple industry datasets, including Meta's Deepfake Detection Challenge Dataset (DFDC). FACIA's proprietary algorithm was tested across more than 100,000 images and videos, reporting an overall detection accuracy of 99.6%. The announcement comes amid growing global concern over synthetic media. In 2023 alone, the number of deepfake videos tripled, with total deepfake content increasing eightfold. These manipulated assets are now widely used in fraud schemes, misinformation campaigns, and non-consensual content creation, prompting regulatory responses worldwide.
Yahoo
22-07-2025
- Business
- Yahoo
FACIA Reports 100% Accuracy in Deepfake Detection Across Industry Datasets
LONDON, July 22, 2025 (GLOBE NEWSWIRE) -- FACIA, a global leader in facial biometric technology, announced that its deepfake detection system has achieved 100% accuracy on multiple industry datasets, including Meta's Deepfake Detection Challenge Dataset (DFDC). FACIA's proprietary algorithm was tested across more than 100,000 images and videos, reporting an overall detection accuracy of 99.6%. The announcement comes amid growing global concern over synthetic media. In 2023 alone, the number of deepfake videos tripled, with total deepfake content increasing eightfold. These manipulated assets are now widely used in fraud schemes, misinformation campaigns, and non-consensual content creation, prompting regulatory responses worldwide. FACIA's model delivered perfect classification on the DFDC dataset, which includes 2,100 manipulated videos using eight different facial alteration techniques. Additional testing was conducted on FACIA's internal dataset of 3,430 AI-generated images created using tools like Midjourney, Artbreeder, and achieving 89.01% accuracy. Further tests on four leading open-source deepfake datasets contributed to the model's combined detection accuracy of 99.6%. FACIA highlighted the system's performance across varying conditions, noting its relevance to sectors like finance, defense, and immigration. 'This isn't just about setting a benchmark,' said Daniyal Assad Chughtai, CTO at FACIA. 'The rate at which deepfakes are spreading makes real-time, scalable detection infrastructure a critical need for social platforms, financial services, and public agencies.' FACIA's system differs from many current solutions by moving beyond frame-by-frame analysis and passive liveness detection. Instead, it uses a multi-layered detection pipeline specifically built to counter modern deepfake threats. The company also maintains low false acceptance and rejection rates, essential for high-assurance identity applications. Looking ahead, FACIA plans to expand its detection capabilities with multilingual spoof detection, broader dataset training, and enhanced API support for integration into third-party platforms. The announcement arrives as major platforms like Meta, TikTok, and X (formerly Twitter) face mounting scrutiny over synthetic media and misinformation. FACIA is currently offering live demonstrations of its technology to partners and customers in deepfake-vulnerable sectors. About FACIA Facia provides deepfake prevention and detection solutions across 190+ countries. Its offerings include 3D liveness detection, age estimation, and iris recognition, built with some of the market's fastest, most accurate algorithms. Media Contact Ans AbbasMarketing Lead | Faciaans@ A photo accompanying this announcement is available at in retrieving data Sign in to access your portfolio Error in retrieving data
