Deepfake Detection Software
A software to detect deepfakes & prevent identity spoofing
In the landscape of advanced digital manipulation, reliable deepfake detection software is highly in demand. BioID employs sophisticated algorithms, leveraging artificial intelligence to verify the authenticity of visual content. This guarantees a strong defense against the growing threat of deceptive media in identity verification processes.
What is Deepfake Detection?
Deepfake detection involves identifying AI-generated content, such as realistic videos or images, created by using deep learning techniques. Methods include forensic analysis, biometric comparison, machine learning models, and specialized tools. These methods distinguish between authentic and manipulated fake media. As deepfake technology advances, efforts aim to detect misuse and maintain trust in multimedia.
Why Deepfake Detection?
Deepfake detection systems are the best way to combat malicious ai-generated media content. By implementing deepfake identification, individuals and organizations can minimize the risks of misinformation, privacy breaches, and potential manipulation of public opinion. This maintains the integrity of and trust in the digital world.
How does it work?
The technology typically uses machine learning algorithms to analyze facial features, gestures, and other elements in videos. These algorithms are trained on large datasets real and synthetic (deepfake) videos. After identifying the patterns and anomalies of the dataset, the detection distinguishes between genuine and manipulated content. Additionally, some software also analyzes inconsistencies in lighting, shadows, and reflections.
What kinds of Face Deepfakes do exist?
AI-generated face deepfakes convincingly manipulate facial features. An MDPI survey identifies four main types of such: Identity swap (replacing one’s face with another). Face reenactment (transferring facial expressions and movements from one person to another). Attribute manipulation (modifying facial attributes like age, gender, hair color, or facial hair and thus altering a person’s appearance). And face synthesis (generative AI that creates a complete new face, crafts a lifelike and unique identity of a non-existent person).
How does Deepfake Detection work?
Detecting Deepfakes explained in 3 Steps
Face Data Collection
Generating ethnical and gender-diverse data sets of real and (deep) fake faces.
Training AI to recognize the differences between authentic and potentially manipulated content.
Integrating deepfake detection software tools into verification services for liveness detection and biometric authentication.
Key Benefits of Deepfake Detection
Identity verification through
passive liveness detection.
Time & Cost Effective
Self-service identity verification makes manual review superfluous.
Best-in-class biometric accuracy
at high assurance levels.
Trustworthy identification of malicious activities.
Detect deepfakes and try all BioID services in a free trial on the playground!
Our Investment to Combat
Fake ID – German Funded Research
There are ongoing researches actively trying methods to directly spot deepfakes in photos and videos. As part of this effort, BioID is collaborating with an AI security research consortium funded by the German Federal Ministry of Education and Research (BMBF).
The main objective of the FAKE-ID consortium is to develop a deepfake detection software using artificial intelligence (AI). Deepfakes and the procedures involved in their creation are examined thoroughly with the aim to develop generative AI detection algorithms. In addition to a supporting mechanism for legislative, the deepfake detectors can be used to verify identities remotely. In terms of legal and ethical argumentation, the consortium carefully examines how detection software affects individuals’ rights and society as a whole.
Deepfake and Identity Fraud Defense
New datasets are regularly generated to improve deepfake technology, drawing from millions of images trained by various apps like: Dall-E, Midjourney, Stable diffusion. And Fun apps such as Animafy, Animate, Avatarify, Copyface, DeepfakeStudio, FaceApp, Facefy, FacePlay, Impressions, Jiggy, MugLife, MyHeritage, Nostalgia, Reface, VidAvatar, Wombo, and Xpression.
To counter these advancements, BioID continuously updates its Liveness Detection, including defenses against deepfakes and identity fraud, while meeting ISO/IEC 30107-3 standards.
What’s the Difference between Deepfakes & Cheapfakes?
Deepfakes and cheapfakes represent two distinct categories of deceptive face images. Each are characterized by their differences of sophistication and intent. Deepfakes (“deep learning fakes”), are advanced machine learning techniques and neural networks that generate highly realistic visual content alterations. The technology seamlessly replaces faces, posing a significant challenge for detection. In contrast, cheapfakes involve less technologically sophisticated methods. They often utilize basic editing tools. Despite being less convincing than deepfakes, cheapfakes can still deceive, particularly on social media. To combat both methods, a reliable deepfake verification technology is needed. BioID’s liveness detection model is able to identify fake images and videos of persons and authenticate a real individual as such.
Made in Germany
Originating from the research institute Fraunhofer IIS in 1998, BioID is a German biometrics company.
Our technology has a proven record since its inception and is trusted by countless enterprises, banks, and government organizations.