Synthetic Data Generator for Bias-Free Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer vision-based anomaly detection systems face challenges in training bias-free models due to the scarcity and skewness of anomaly or crime data, leading to ineffective detection across various demographics.
Innovation Solution
An intelligent system utilizing domain adaptation and generative adversarial networks to train computing devices with synthetic and real data, enabling the generation of indistinguishable synthetic data that resembles real data, thus allowing for bias-free anomaly detection, including criminal activity recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real anomaly data is used to train the model, then the model can detect real anomalies, but the data is scarce and skewed toward certain demographics causing bias
Solution Approach 1:
The patent creates synthetic anomaly data that copies the structural characteristics of real anomaly data while eliminating demographic biases. The synthetic data generator produces annotated synthetic images that replicate anomaly patterns without the skews present in real-world data, enabling fair detection across all demographics.
Solution Approach 2:
The patent introduces synthetic data as an intermediary between real anomaly data and the training model. This synthetic intermediary layer provides balanced training examples that bridge the gap between scarce real data and the need for diverse, fair detection capabilities.
2Adaptability or versatility
If more diverse training data is collected, then bias-free detection is achieved, but data collection becomes more burdensome and time-consuming
Solution Approach 1:
The patent performs preliminary data generation by creating synthetic anomaly data in advance before actual anomaly detection is needed. This pre-generated synthetic data can be immediately used for training, eliminating the time-consuming process of collecting diverse real-world anomaly data.
Solution Approach 2:
The synthetic data generator creates copies of anomaly patterns that capture essential features without requiring actual occurrence of diverse criminal events. These copies provide sufficient training data for fair detection while avoiding the time and ethical challenges of collecting real diverse anomaly data.
3Productivity
If synthetic data is generated to resemble real data, then training efficiency improves, but the complexity of the system increases
Solution Approach 1:
The synthetic data generator creates visual copies of real anomaly data that are indistinguishable from real data to the discriminator. This copying approach enables efficient training using synthetic data while maintaining a relatively simple system architecture focused on image generation and classification.
Solution Approach 2:
The synthetic data serves as an intermediary training resource that simplifies the overall system by providing ready-to-use training examples. This intermediary layer reduces the complexity of data collection and processing while maintaining training efficiency through automated synthetic data generation.
Data Source
AI summary
An intelligent system and method for anomaly, such as crime, detection is provided. In some embodiments, the system may comprise a computing device, defined by a generator and a discriminator, and at least one video camera. The generator may generate synthetic data and real data and, in turn, the discriminator may evaluate and classify the synthetic data and the real data as real or synthetic. In other embodiments, the computing device may be trained so as to classify the synthetic data and the real data as normal or anomaly. In further exemplary embodiments, the video camera may capture a plurality of live action events and generate video data, which the video camera may then transmit to the computing device. The computing device may then classify the live action events as normal or anomaly. In embodiments where the live action events may be classified as anomaly, an appropriate authority may be notified so as to provide decreased response times and ultimately, improve safety and prevent crimes.