Sumsub can tell apart deepfakes from real photos on the fly, no matter how realistic they are. Discover if you can do the same in our whack-a-deepfake minigame.
Try your hand at deepfake detection and guess which of these photos are real. See how many you can find in 45 seconds.
The results are calculated based on both speed and precision. Try to get at least 9 correct answers!
A deepfake is a graphical output produced by machine learning. It can be any kind of image: from crude drawings to photo-realistic portraits indistinguishable from reality.
Synthetic fraud utilizes deepfake technology to a wider extent. Fake news, the Pope wearing Balenciaga, or a carefully crafted fake ID are all examples of synthetic fraud.
Deepfake use is on the rise everywhere. In Q1 2023, there were 10% more deepfake attacks than in all of 2022.
*Sumsub's internal data covering Q1 2023 to present day
Synthetic fraud detection is a core feature of Sumsub’s Liveness technology. Any graphical artifact is instantly recognized regardless of complexity. Deepfakes can rotate their heads during Liveness checks too, after all.
Send us a message and Sumsub’s experts will help you instantly detect any deepfakes and synthetic fraud, no matter how advanced.
An example of synthetic identity fraud is when a fraudster combines real and fake information to create a new identity. They may use a legitimate Social Security number or other personal details combined with fabricated data or ID photos to perform fraud. Companies can prevent synthetic identity fraud with biometric spoofing and deepfake detection software.
With the help of Sumsub’s Liveness and Face Match technologies, synthetic fraud can be instantly detected via automated analysis of graphical artifacts. Synthetic ID fraud detection is a necessary procedure for regulated businesses to undertake during client onboarding.
Deepfake refers to the use of artificial intelligence and machine learning to create highly realistic manipulated videos, images, and audio recordings. Deepfakes can be used to deceive individuals or manipulate public opinion by impersonating someone and spreading false information. As a result, deepfake cybersecurity efforts are concentrated on preventing this particular type of fraud from taking place.
Deepfakes can be employed for fraud, blackmail, or social engineering purposes. They have the potential to undermine trust in media and erode the credibility of visual or audio evidence. These risks can be negated with a specialized deepfake detection tool like Sumsub.
Deepfake detection software like Sumsub employs machine learning algorithms and forensic analysis to detect anomalies in manipulated media. As deepfake technology advances, so does the sophistication of detection methods, resulting in an ongoing cat-and-mouse game between fraudsters and detectors.