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Telecommunications Operator Scenario

Efficiently detecting and identifying deepfake content encountered by operators
Telecommunications Operator Scenario

What types of deepfakes exist?

索引

  • Deepfake Content Classification
    Deepfake Content Classification
  • Risks of Deepfake Technology in Telecommunications Operator Scenarios
  • Telecommunications Operator Scenario Forgery Detection Solutions
What types of deepfakes exist?

Deepfake Content Classification

Deepfakes can generally be divided into four categories: reenactment, replacement, editing, and synthesis. Reenactment refers to capturing target person's movements, expressions and other information and reproducing these movements and expressions on other people or virtual characters; replacement refers to replacing specific elements in images, videos or audio, such as faces, voices, etc., with other elements, and the replaced content has high visual or auditory realism. Editing involves modifying and editing existing images, videos or audio content, including adjusting character postures, modifying text content, changing scene elements, etc. Synthesis refers to generating entirely new images, videos, audio or text content through algorithms.

Risks of Deepfake Technology in Telecommunications Operator Scenarios

As core providers of information and communication services, telecommunications operators possess massive user data and communication network resources. Their security and reliability directly relate to user interests and social stability. The abuse of deepfake technology may lead to a series of problems including user privacy leakage, intensified content security risks, prominent legal compliance issues, and damaged user trust.

User Privacy and Security Risks

  • Criminals use deepfake technology to forge users' biometric information such as faces and voices, breaking through operators' identity verification systems and stealing user identities for malicious operations.

Content Security Risks

  • Deepfake technology can generate realistic fake video, audio and image content, which may be maliciously spread, causing users to receive incorrect information.

Legal and Compliance Risks

  • With the development of deepfake technology, relevant laws and regulations continue to improve. If operators fail to effectively control risks brought by deepfake technology, they may face regulatory penalties.
  • Deepfake methods continue to evolve with new algorithms and techniques emerging frequently, making detection increasingly difficult. This requires detection technology to not only identify known forgery patterns but also proactively respond to new types of forgery methods and accurately identify forged content.

Telecommunications Operator Scenario Forgery Detection Solutions

Addressing current industry pain points such as insufficient generalization capability in forgery detection, weak active protection mechanisms, and incomplete traceability systems, by integrating multimodal deepfake detection, active protection and tracking traceability capabilities, a comprehensive governance solution covering data layer to model layer to system layer has been formed.

Multimodal Sample Construction

At the data layer, the platform gathers and constructs high-quality, multimodal forgery sample sets, covering various data types such as images, audio, and video, comprehensively covering 90% of mainstream forgery algorithms including replacement, reenactment, and generation types, significantly improving detection system generalization capabilities in complex scenarios and laying a solid data foundation for precise identification.

High Generalization Protection System

At the model layer, combining multimodal detection technologies such as vision, speech, and text, and introducing generalization enhancement strategies, an active protection system with adaptive capabilities is constructed. This system can continuously learn and evolve, effectively resisting new and unknown forgery methods, ensuring leading advantages in confronting continuously evolving deepfake technologies.

Precise Traceability Analysis

At the system layer, based on multi-dimensional information such as identity fingerprints, model fingerprints, and device fingerprints, precisely track the generation paths of forged content, achieving fine-grained traceability analysis and propagation chain analysis. Through this systematic traceability mechanism, the source of forged content can be quickly locked, improving overall security protection capabilities.