Use Appdome’s AI-Native platform to secure, monitor, and respond with Deepfake Detection in Android & iOS apps fast. Let AI code and build Certified Secure™ deepfake detection into your mobile apps to prevent deepfakes, deepfake methods, video injections, DMA attacks, and more. Build a perimeter defense around the facial recognition and identitiy verification in your mobile app. Preserve the integrity of mobile authentication. No SDKs, coding, or servers required. Automate everything. Save Money.
Use AI to code and compile deepfake detection features in Android & iOS apps. Face recognition is vulnerable to deepfakes. With Appdome, build a perimeter defense around facial recognition to prevent deepfakes and ensure the authenticity of user authentication. No code, SDKs, or servers required.
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With Appdome's Threat-Events™ framework, mobile brands can detect deepfake threats throughout the mobile application lifecycle, from onboarding to sign-in, payment, and more. Use the deepfake signals to tailor and control the authentication experience to keep mobile users and the business safe.
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ThreatScope™ XTM monitors the active attack surface of your mobile business, looking for deepfake attacks, the impact of deepfake defenses, and emerging deepfake and biometric threats, giving you the power to preempt any facial recognition bypass or ATO attacks from deepfakes with ease.
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Easy to use. Most defenses. Fastest time to market. These are just some of the things our customers say about using Appdome for Deepfake Detection. On top of that, the industry has awarded us over 20+ awards covering everything from Most Innovative, Best Support, and Best in Class for Mobile Anti-Spyware Defense, Security, Anti-Fraud, DevOps, Bot Defense, XDR and more.
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Appdome uses AI and a modular architecture to bring efficiency and scale to the mobile deepfake detection lifecycle. On one platform, mobile businesses build, monitor, and respond with 400+ mobile app security, anti-fraud, anti-ATO and anti-bot defense plugins in Android & iOS apps on demand. Each mobile Deepfake Detection plugin automatically adjusts to the code of the app and uses a dynamic defense model that analyzes behavioral anomalies, identifies threats, and filters out false positives, all without a server or external attestation. If you want to eliminate big Epics and manual work, handoffs, and resolutions in your mobile deepfake detection journey, Appdome is the right choice.
Face ID, or LocalAuthentication, in iOS and Face Unlock, or BiometricPrompt, in Android are the most widely used facial recognition systems inside Android & iOS apps. These systems are the gateway for autofill in the login sequence for mobile apps, offering security and convenience for mobile end users. Unfortunately, they are also targets to attackers looking to use deepfakes or perform facial recognition bypass attacks. Appdome defends the Face ID and Face Unlock sequence by detecting any attempt to capture, manipulate or interfere with the mobile device LocalAuthentication or BiometricPrompt. This ensures the integrity of local face recognition and prevents Deepfake attacks with ease.
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Commercial biometric identity verification systems are vulnerable to deepfakes. These systems compare a live face capture with stored images from government-issued IDs such as a passport or driver's license. But, by the time these services are called, the depfake attack is already in place. Appdome creates a perimeter defense around these biometric identity verification services, monitoring the application, biometric API calls, memory space, image buffers, and other elements of the biometric authentication chain for signs of deepfake attacks. If an threat is present, the app can respond before the deepfake attack is initiated and before calling the identity verification system.
Appdome secures mobile user authentication in Android & iOS apps by monitoring the biometric data stores used in facial recognition systems for signs of manipulation or exploits. In Android, this means identifying exploits in SurfaceFlinger, and the Camera Hardware Abstraction Layer (HAL). In iOS, it means detecting manipulation of Apple’s FaceID Keychain, Metal Framework and AVCaptureSession. In both, it means detecting Direct Memory Access (DMA) attacks, image buffer manipulations, memory hooking and process injection attacks. The goal of these methods is to detect the points of ingress and egress where deepfake image and video content can be injected, superimposed or accessed by evaluation systems.
Appdome's Detect Deepfake Apps protects mobile apps from deepfake attacks by detecting or blocking the use of deepfake apps and face swap apps in Android and iOS apps. This feature identifies and prevents deepfake tools used to spoof facial recognition systems such as DeepFaceLab, DeepFaceLive, Avatarify, Deefake Studio, FaceSwap-GAN, FaceMagic, Reface, Zao, and other face swap apps, all of which can be used to manipulate biometric data and perform deepfake biometric bypass attacks. Detect Deepfake Apps also detects virtual camera redirection or substitution, a technique typically used in combination with deepfake and face-swapping tools. Data about each exploit attempt can be passed to the application for mitigation steps.
Appdome provides Deepfake Video Detection for mobile apps. This feature identifies video injection and frame injection techniques used to bypass face recognition and identity verification systems. These attack vectors typically inject fake live videos or fake still images directly into the camera stream to fool facial recognition and liveness detection systems. The defense includes detecting fake faces, pre-recorded or AI-generated video content into the camera buffer to impersonate users during ID verification (e.g., KYC fraud, deepfake banking fraud) and more. This defnese can be augmented with other Appdome features that detection hooking, patching, swizzling and other methods.
Appdome deepfake detection suite can monitor the mobile camera and device sensors to detect deepfake liveness bypass and other adversarial techniques such as manipulating embeddings and encodings in facial recognition data, lighting meter, camera focus, etc. To do this, Appdome compares the device sensors like accelerometer, gyroscope, gravity sensor, and more, as well as the mobile device and application state, with the camera inputs to determine mismatches with the biometric process, feeds, or data. For example, if the application is receiving a camera feed but is in the background, the likelihood of a deepfake attack rises. This data is sent to the app for mitigation steps.
Adversarial Deepfake Detection protects mobile apps by identifying subtle, AI-driven manipulations designed to fool facial recognition systems. Appdome defends against these techniques by monitoring for embedding anomalies, irregular similarity scores, and model-level inconsistencies that indicate adversarial interference. It also blocks attempts to inject tampered frames, override facial recognition APIs, or manipulate authentication logic via synthetic facial data. By securing the biometric pipeline at the model and inference levels, this feature prevents unauthorized access and shields mobile apps from stealthy, AI-powered deepfake exploits.
Appdome Liveness Detection ensures user authenticity by applying primary or secondary liveness checks during the facial recognition process. It verifies 3D depth, skin texture, lighting, eye reflectiveness, and the strength of liveness images to confirm the presence of a real, live face. This real-time detection prevents spoofing attempts, such as static images, videos, or deepfake content, ensuring the integrity of biometric authentication systems. Complementary to third-party liveness solutions, Appdome provides an additional layer of protection to enhance the security of mobile apps and safeguard users against fraudulent access.
With Appdome Threat-Events™, mobile brands and developers can get rich threat data directly from the Appdome framework in the app, keep full control over the user experience, and enjoy multiple threat response options when mobile deepfakes are detected. Threat-Events™ enables the app to plug into and control Appdome's deepfake detection methods and threat data, and use the threat data to tailor in-app responses and mitigation workflows based on the specific deepfake threats present in the application lifecycle.
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Inside a highly demanding DevOps lifecycle, getting deepfake defense right is extremely hard. Mobile apps are updated 24x-36x a year, the Android & iOS OS changes frequently, and threats evolve constantly. Appdome uses AI to eliminate this complexity, implement and maintain each mobile deepfake defense up to date, and support the mobile engineering team's autonomy and release cycles. Full support for the Mobile DevOps tool chain and best practices is a standard part of using Appdome.
With Appdome, you can meet Deepfake Detection requirements without sacrificing your engineering freedom, development choices, other features, or the user experience.
Appdome uses AI to create and build Deepfake Detection that works with the way you’ve built your app, including the coding languages and frameworks used in your Android apps. Appdome also supports your existing DevOps tech stack, including CI/CD, test automation, release management, and more.
Need to deliver Deepfake Detection without a lot of work, crashing your app or slowing down your release cycle? We’ve got you covered.
Get a price quote and start saving money on deepfake detection today. Appdome’s deepfake detection & prevention solution helps mobile brands save $millions of dollars by avoiding unnecessary SDKs, server-side deployments, engineering work, support complexity, code changes and more.
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