Systems and methods for authenticating via photo modification identification
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Solution Overview
Problem
Existing artificial intelligence-based authentication systems face challenges due to the complexity of obtaining high-quality training data, the need for specialized knowledge to design and integrate these systems, and the difficulty in reviewing results, making them vulnerable to nefarious users who can easily circumvent authentication by guessing common features.
Innovation Solution
A system that generates high-quality training data by processing existing images to determine similarity metrics, modifies images using generative adversarial networks, and requires users to select modified features to authenticate, reducing the ability of nefarious users to circumvent the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing authentication systems use common features (e.g., bikes, cars) for user verification, then authentication can be performed using available training data, but the system becomes vulnerable to circumvention by nefarious users who can easily guess these common labels
Solution Approach 1:
The system transitions from using common authentication features that are uniformly vulnerable to guessing, to using personalized local features specific to each user's media collection. By modifying images based on user-specific content rather than generic features, the system creates localized security measures that are difficult to circumvent while maintaining authentication functionality.
Solution Approach 2:
The system performs preliminary modification of user media items before authentication attempts. By pre-modifying images with generative adversarial networks and storing both original and modified versions, the system prepares authentication challenges in advance, ensuring that when authentication is needed, the user must identify modifications to their own pre-modified content, which they can recognize but others cannot easily guess.
2Reliability
If the system requires users to select features known only to them (e.g., personal photos), then authentication security is enhanced, but the complexity of obtaining and processing high-quality training data increases
Solution Approach 1:
The system uses each user's own media collection as the basis for their authentication challenges. Instead of requiring the system to source and verify external training data for each user, the user's existing media library serves as the authentication substrate. This self-service approach eliminates the need for complex external data procurement while maintaining security through personalized content.
Solution Approach 2:
The system implements a multi-functional pipeline that simultaneously performs similarity metric calculation, image modification, and authentication challenge generation using the same user media data. This universal processing approach allows the system to derive multiple authentication-related outputs from a single data source, reducing overall complexity while maintaining security.
3Reliability
If generative adversarial networks are used to modify user images for authentication, then the ability to prevent malicious authentication is improved, but the need for specialized knowledge to design and integrate the system increases
Solution Approach 1:
The system introduces an intermediary training data generation step that bridges the gap between user media and GAN modification. By first calculating similarity metrics and selecting appropriate media based on these metrics, the system creates a curated intermediate dataset that guides the GAN modification process. This intermediary layer simplifies the overall system by providing structured input to the GAN, reducing the specialized knowledge needed for integration.
4Reliability
If the system processes user media to generate similarity metrics and modify images, then authentication security is enhanced, but the time and resources required for data processing increase
Solution Approach 1:
The system performs similarity metric calculation and image modification in advance, before authentication is actually needed. By pre-processing user media to create modified versions and storing both originals and modifications, the system eliminates the need for time-consuming processing during authentication events. The preliminary action ensures that when authentication is required, the system can quickly present pre-modified images for user verification.
Data Source
AI summary
Methods and systems described herein relate to authenticating users-based on generating images that have modified features. More specifically, the methods and systems generate these images by processing existing training data to identify a common feature in existing photos that may be modified, and then modifying that feature with the use of generative adversarial networks.