Seamless Liveness Detection in Your Browser with Didit's JS SDK

Effortless IntegrationDidit's JS SDK allows developers to embed advanced Liveness Detection directly into web applications with minimal code, supporting both passive and active methods.

Robust Fraud PreventionLeverage AI-native anti-spoofing technologies, including 3D Action & Flash, 3D Flash, and Passive Liveness, to combat sophisticated presentation attacks like deepfakes and masks.

Configurable SecurityTailor liveness checks with customizable thresholds for scores, face quality, and luminance, allowing businesses to balance security needs with user experience.

Comprehensive InsightsReceive detailed JSON reports on each liveness check, including status, method, score, and specific warnings, enabling informed decision-making and streamlined risk assessment.

The Growing Need for Browser-Based Liveness Detection

In today's digital-first world, businesses are constantly battling sophisticated fraudsters. From account takeovers to synthetic identity fraud, the methods used by bad actors are evolving rapidly. A critical line of defense in this fight is liveness detection, which verifies that the person interacting with a system is a real, live individual and not a spoofing attempt using a photo, video, or even an advanced deepfake. While mobile apps have long benefited from integrated liveness checks, bringing this capability directly into the browser is essential for a seamless and secure web experience.

Browser-based liveness detection eliminates the need for users to switch to a separate app, reducing friction in onboarding and authentication flows. However, implementing such a system comes with its own set of challenges, including ensuring compatibility across various browsers, managing performance, and maintaining high accuracy against diverse attack vectors. Didit's Liveness Detection, powered by its AI-native platform, addresses these challenges head-on, offering 99.9% accuracy and a False Acceptance Rate (FAR) of less than 0.1%.

Understanding Didit's Liveness Detection Methods

Didit provides a suite of Liveness Detection methods through its JS SDK, designed to cater to different security requirements and user experience preferences. Each method employs advanced computer vision and machine learning algorithms to distinguish between a live person and a presentation attack.

• Passive Liveness: This method offers the lowest friction, requiring only a single frame analysis. It examines the image for artifacts, texture patterns, and subtle indicators to detect spoofing attempts from photos or digital screens. Ideal for low-risk scenarios where speed and convenience are paramount.

• 3D Flash: A higher security option, 3D Flash projects a series of dynamic light patterns onto the user's face. By analyzing the reflections at over 30 frames per second, it creates a depth map to confirm the face's three-dimensional structure, effectively defeating flat images or 2D spoofs. It provides a seamless experience without explicit user interaction.

• 3D Action & Flash: Offering the highest level of security, this method combines dynamic light pattern analysis with a randomized action sequence (e.g., blinking or nodding). Deep learning algorithms analyze both micro-expressions and light reflection responses. This dual-factor approach makes it exceptionally difficult to spoof, even with advanced masks or deepfakes, making it suitable for high-security applications like banking or healthcare.

The Didit JS SDK allows developers to easily integrate these methods, providing the flexibility to choose the right balance of security and user experience for their specific use cases.

Integrating Liveness Detection with Didit's JS SDK

Integrating Didit's Liveness Detection into your web application using the JS SDK is straightforward and developer-friendly. The SDK handles the complexities of camera access, image capture, and secure communication with Didit's backend, allowing you to focus on your application's core logic. The modular architecture of Didit's platform means you can easily plug liveness checks into your existing identity workflows.

The process typically involves initializing the SDK, prompting the user for camera access, guiding them through the chosen liveness challenge (if active), and then submitting the captured data for analysis. The SDK provides real-time feedback to the user, enhancing the overall experience. Upon completion, the backend returns a comprehensive JSON report containing the liveness status, method used, a confidence score, and any detected warnings. This detailed report empowers businesses to make informed decisions, whether to automatically approve, decline, or flag a session for manual review.

Interpreting Liveness Reports and Managing Risk

A key advantage of Didit's Liveness Detection is the detailed insights provided in each verification report. The JSON response includes critical fields such as status (Approved, Declined, In Review, Not Finished), method, score, and a warnings array. The score indicates the confidence level of the liveness detection, while warnings provide specific details about potential risks or issues detected during the process.

For instance, warnings like LIVENESS_FACE_ATTACK indicate a potential spoofing attempt, leading to an automatic decline. Other warnings, such as LOW_LIVENESS_SCORE or POSSIBLE_DUPLICATED_FACE, can be configured by the application to trigger an 'In Review' status or an automatic decline based on predefined thresholds. This granular control allows businesses to fine-tune their risk management strategies. Didit's platform also features configurable verification settings for issues like duplicate faces, multiple faces detected (for Passive Liveness), face quality, and face luminance, providing unparalleled flexibility in adapting to varied security policies.

How Didit Helps

Didit provides an unparalleled solution for implementing liveness detection in the browser. Our AI-native platform offers superior accuracy and robust fraud prevention capabilities, including Passive & Active Liveness detection. With Didit's modular architecture, you can seamlessly integrate these advanced biometric checks into any workflow, whether through our clean APIs or the no-code Business Console. We stand out by offering Free Core KYC, allowing you to start verifying identities without upfront costs. There are no setup fees, and our pay-per-successful-check model ensures you only pay for what you use. Beyond liveness, Didit offers a full suite of identity verification tools, including ID Verification, 1:1 Face Match, AML Screening, and Age Estimation, all designed to automate trust and orchestrate risk globally and at scale.

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