Layered Fraud Detection for Deepfake, Morph, and Face-Swap Attacks
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Solution Overview
Problem
Existing fraud detection systems struggle to effectively identify deepfake, face morph, and face swap attacks due to the lack of sufficient training data and the ease of creating such images using available tools, making it difficult for machine learning models to detect these types of fraud.
Innovation Solution
A system and method utilizing a layered architectural approach with multiple detection types, including deepfake, face morph, and face swap models, combined with image processing techniques and liveness checks, to analyze image data and generate an aggregated fraud score for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple detection types are performed on image data to improve fraud detection accuracy, then the reliability of fraud detection is improved, but the device complexity increases
Solution Approach 1:
The fraud detection system is segmented into multiple independent detection types (deepfake detection, face morph detection, face swap detection, injection attack detection). Each detection type is implemented as a separate model that processes image data independently, then their results are aggregated. This segmentation allows the system to achieve high reliability through multiple specialized detectors while managing complexity by modularizing each detection function.
Solution Approach 2:
Multiple detection models are merged into a unified fraud detection system that aggregates their outputs. The deepfake model, face morph model, face swap model, and injection attack model all process the same image data and their results are combined to generate an aggregated fraud score. This merging approach improves reliability through ensemble methodology while the aggregation mechanism provides a clear integration strategy.
2Adaptability or versatility
If machine learning models are trained on diverse attack types to improve detection capability, then the adaptability to various fraud types is improved, but the difficulty of obtaining training data increases
Solution Approach 1:
The system uses synthetic data generation to create copies of fraudulent images for training purposes. Since obtaining real fraud data is difficult, the system generates synthetic deepfake, face morph, face swap, and injection attack images using AI models during the training phase. This copying approach enables the system to train on diverse attack types without requiring access to actual fraud data, thereby improving adaptability while resolving the data acquisition difficulty.
3Measurement precision
If multiple detection models are applied to image data to enhance detection accuracy, then the measurement precision of fraud detection is improved, but the use of computational energy increases
Solution Approach 1:
The system applies multiple detection models (deepfake, face morph, face swap, injection attack) to the same image data, which is an example of excessive action. By running more detection models than a single model would provide, the system achieves higher measurement precision through ensemble methodology. The partial action approach would be to apply only one detection model, but the patent chooses excessive action to ensure comprehensive coverage of multiple fraud types, accepting the increased computational energy cost as necessary for high-accuracy detection.
Data Source
AI summary
A system and method for multiple fraud type detection includes anti-injection attack system that has a layered architectural approach that uses a includes combination of different specific models to detect the attacks in combination with image processing techniques, device signals and liveness checks to detect the variety of different types of fraud attacks or repeat fraud attacks. The anti-injection attack system applies the analysis of the tools used to create deepfake, face morph and face swap attacks to define the elements of its layered architecture that can these various types of attacks.