Deep Image Comparison for Localizing Editorial Image Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image comparison systems are vulnerable to adversarial attacks, computationally expensive, and struggle to accurately distinguish between benign and editorial image modifications, particularly in the context of online image distribution.
Innovation Solution
An image comparison system utilizing a deep image comparator model and an image retrieval model that employs a visual search embedding trained with contrastive loss to robustly identify and localize editorial changes in images, while ignoring benign transformations, and a deep image retrieval model for near-duplicate image search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image comparison systems are used, then computational resources are consumed, but accuracy in distinguishing editorial from benign modifications deteriorates
Solution Approach 1:
The system segments the image comparison task into two distinct stages: first, a deep image retrieval model performs near-duplicate image search to identify candidate images, and second, a deep image comparator model performs detailed editorial change detection only on retrieved candidates. This segmentation reduces overall computational cost by avoiding full pairwise comparison of all images while maintaining high detection accuracy through focused analysis of relevant candidates.
Solution Approach 2:
The system performs preliminary filtering using the deep image retrieval model to identify near-duplicate images before applying the computationally intensive deep image comparator model. This preliminary action of retrieving candidate images based on visual similarity ensures that the expensive editorial change detection algorithm is only applied to relevant image pairs, significantly reducing total computational resource consumption while preserving accuracy.
2Measurement precision
If deep learning models are deployed for image comparison, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system divides the complex deep learning-based image comparison task into two specialized models: a deep image retrieval model for near-duplicate search and a deep image comparator model for editorial change detection. Each model is optimized for its specific function, reducing individual model complexity while maintaining overall system accuracy through the coordinated two-stage process.
Solution Approach 2:
The deep image retrieval model acts as an intermediary between the input images and the deep image comparator model. It retrieves near-duplicate candidates that serve as intermediates for the subsequent editorial change detection step, thereby simplifying the overall system architecture by breaking down the complex task into manageable stages with clear interfaces between components.
3Reliability
If robust image retrieval is implemented, then near-duplicate identification accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary near-duplicate image retrieval using the deep image retrieval model before applying the computationally intensive editorial change detection. This preliminary action identifies candidate images with high reliability, ensuring that subsequent detailed analysis is only performed on relevant matches, thereby reducing overall processing time while maintaining high identification accuracy.
Solution Approach 2:
The system segments the image processing workflow into a fast preliminary retrieval stage using the deep image retrieval model and a slower detailed analysis stage using the deep image comparator model. This segmentation allows the system to quickly filter large image collections to identify near-duplicates with high reliability, then focus computational resources on detailed editorial change detection only for the retrieved candidates, optimizing the trade-off between accuracy and processing time.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to identify regions of an image that have been editorially modified. For example, the image comparison system includes a deep image comparator model that compares a pair of images and localizes regions that have been editorially manipulated relative to an original or trusted image. More specifically, the deep image comparator model generates and surfaces visual indications of the location of such editorial changes on the modified image. The deep image comparator model is robust and ignores discrepancies due to benign image transformations that commonly occur during electronic image distribution. The image comparison system optionally includes an image retrieval model utilizes a visual search embedding that is robust to minor manipulations or benign modifications of images. The image retrieval model utilizes a visual search embedding for an image to robustly identify near duplicate images.