AI Voice Obfuscation Encoding for Impersonation Resistance
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Solution Overview
Problem
Spoofing of audio and video communications becomes prevalent with the advent of machine learning and artificial intelligence, posing risks of identity theft and malware through impersonation attacks, as attackers use harvested voice and video samples to deceive callees and callers.
Innovation Solution
Implementing voice obfuscation techniques using AI/ML models on user equipment (UE) and network devices to alter voice characteristics, such as pitch, tone, and note, by replacing user-specific words with AI-generated ones and applying random values to create obfuscation, ensuring the voice is not misused for impersonation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice obfuscation is implemented using AI/ML models to alter voice characteristics, then security against spoofing attacks is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces an intermediary processing layer between the voice signal and the communication channel. AI/ML models serve as intermediaries that analyze voice characteristics and generate obfuscated versions, while random values act as mediators to further obscure the original voice signal. This intermediary approach strengthens security without requiring complete system redesign.
Solution Approach 2:
The patent applies parameter changes by modifying voice characteristics such as pitch, tone, and temporal patterns through AI/ML processing. Random values are applied to transform voice parameters dynamically, creating obfuscated versions that maintain intelligibility while preventing recognition by attackers. This parameter transformation approach enhances security with manageable complexity.
2Reliability
If AI/ML models are used to detect and obfuscate voice characteristics, then protection against impersonation attacks is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by selectively processing only the most critical voice characteristics (pitch, tone, temporal patterns) rather than analyzing every aspect of the voice signal. The system processes voice data in segments and applies obfuscation only when threat detection triggers, reducing overall computational burden while maintaining effective protection.
Solution Approach 2:
The patent implements preliminary action by pre-processing voice characteristics and storing reference data of legitimate voice patterns before actual attacks occur. AI/ML models are trained in advance to recognize spoofing patterns, enabling faster detection and obfuscation during actual communication, thereby reducing real-time computational requirements.
3Reliability
If voice characteristics are altered and obfuscated, then prevention of voice misuse is improved, but communication clarity and naturalness may deteriorate
Solution Approach 1:
The patent applies local quality by selectively obfuscating specific voice characteristics (pitch, tone, temporal patterns) while preserving others that maintain communication clarity. Different portions of the voice signal receive different levels of processing, with critical security parameters obfuscated and conversational elements preserved, achieving both security and clarity.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the effectiveness of obfuscation and adjusts processing parameters accordingly. If obfuscation causes excessive distortion, the system reduces processing intensity or switches to alternative obfuscation methods, maintaining communication quality while preserving security protection.
Data Source
AI summary
In some implementations, a device may detect a voice call involving a user. The device may identify a usage of user-specific language or vocabulary based on a usage pattern. The device may generate, based on the user-specific language or vocabulary, one or more replacement words to replace words spoken by the user during the voice call. The device may generate a random value to be applied to the voice call to create voice obfuscation for the voice call, wherein the random value is used to obfuscate one or more voice characteristics of the voice call. The device may communicate encoded data associated with the voice call, wherein the encoded data is in accordance with the voice obfuscation.