About
Attestiv Deepfake Detector is a comprehensive AI fraud detection platform designed for organizations that rely on digital media to make critical decisions. Unlike shallow, single-layer detection tools, Attestiv employs a composite, multi-model approach that analyzes pixels, metadata, behavioral cues, file context, and temporal anomalies to detect synthetic or manipulated media — including replay attacks that fool legacy systems. The platform covers four major detection domains: deepfake video detection, fake image detection, document fraud detection, and audio authenticity verification. Its Media Provenance Chain traces digital media back to its origin using fingerprinting, making reused or replayed content detectable for compliance and regulatory workflows. The Video Integrity engine measures lip-audio sync anomalies, cross-frame inconsistencies, and generative artifacts over time. For the highest-risk scenarios, Attestiv incorporates a human-in-the-loop escalation pathway, routing ambiguous or high-value media to forensic analysts. The platform is delivered via APIs and an enterprise platform, integrating into existing workflows across insurance claims processing, cybersecurity identity verification, financial services fraud prevention, news and media authenticity verification, and HR background screening. Its explainable AI scoring model is built to adapt to evolving attack methods, including future synthetic replay and multi-modal deception techniques.
Key Features
- Multi-Layered AI Detection: Combines multiple AI and rules-based models for composite fraud scoring, detecting deepfakes that single-point detection tools miss — including replay attacks.
- Content & Context Fusion: Analyzes pixels, metadata, file structure, and behavioral cues together, flagging content that appears real but has suspicious surrounding context.
- Media Provenance Chain: Fingerprints and traces digital media back to its point of origin, making replayed or reused synthetic content identifiable — critical for regulatory compliance.
- Video Integrity Analysis: Detects temporal anomalies across video frames, including lip-audio sync mismatches and cross-frame generated content inconsistencies.
- Human-in-the-Loop Escalation: Escalates ambiguous or high-risk media to forensic human analysts for in-depth review, ensuring high-value decisions are verified beyond automated scoring.
Use Cases
- Insurance companies verifying photo and video evidence in claims submissions to detect AI-generated or manipulated media fraud.
- Financial institutions authenticating identity documents and video KYC submissions to prevent deepfake-driven account takeovers.
- News and media organizations validating the authenticity of user-submitted or third-party video and image content before publication.
- HR and background screening teams verifying the authenticity of video interviews and identity documents submitted by candidates.
- Cybersecurity teams integrating deepfake detection into access control and remote authentication workflows to block replay and synthetic identity attacks.
Pros
- Composite Detection Architecture: Uses multiple AI models rather than a single detection layer, making it significantly more resistant to bypass techniques like replay attacks.
- Broad Media Coverage: Handles video, images, audio, and documents under one platform, removing the need for multiple point solutions.
- Enterprise API Integration: Offers APIs that integrate directly into existing enterprise workflows across insurance, finance, HR, and media industries.
- Provenance-Based Trust: Media provenance fingerprinting adds a compliance-friendly audit trail for regulated industries.
Cons
- Primarily Enterprise-Focused: The platform appears built for large organizations with compliance needs; pricing and access may not be practical for small businesses or individuals.
- No Public Pricing Transparency: Pricing details are not publicly listed, requiring direct contact with the sales team before evaluating cost.
- Human Review Adds Latency: For high-risk scenarios requiring human-in-the-loop review, real-time or automated decision pipelines may be slowed.
Frequently Asked Questions
What types of media can Attestiv analyze?
Attestiv can analyze deepfake videos, fake or manipulated images, fraudulent documents, and audio recordings for signs of AI-generated or synthetically altered content.
How does Attestiv defend against replay attacks?
Attestiv uses a Media Provenance Chain to fingerprint and trace digital media back to its origin. This allows the system to detect when a pre-generated deepfake is replayed to bypass liveness or authenticity checks, unlike legacy single-layer detectors.
What industries is Attestiv designed for?
Attestiv targets insurance (claims fraud), cybersecurity (identity verification), financial services (fraud prevention), news and media (content authenticity), and human resources (background screening).
Does Attestiv offer an API?
Yes. Attestiv provides platform APIs that allow organizations to integrate deepfake and fraud detection directly into their existing applications and workflows.
How is Attestiv different from other deepfake detectors?
Unlike tools that rely on a single detection signal (e.g., eye blinks or facial symmetry), Attestiv uses a composite multi-model approach combining pixel analysis, metadata, behavioral cues, temporal video checks, and human-in-the-loop review for a much more robust and explainable result.
