# Detecting and Mitigating Attacks on GPS Devices
## 6 Countermeasures and Their Comparison
### Countermeasures for GPS-Denied Environment--Alternate Positioning
#### 6.3.6 Generalized Vision Approaches  
vSLAM methods typically operate in specific regions and do not necessarily provide absolute positioning; however, generalized vision-based approaches attempt to provide generalized absolute position estimates. Landmark-based vision approaches attempt to determine position based on simple landmarks; however, generalized vision approaches aim to operate on general terrain imagery to provide absolute positioning, including using latitude and longitude. Consider the case of a UAS-based system, where a UAS-based camera sensor observes the ground and an onboard neural network-based algorithm performs terrain recognition to determine the system's position. The authors of [196] proposed an approach where camera imagery was matched to pre-existing aerial satellite imagery to determine location. Factors such as weather, lighting conditions, and seasonal changes to terrain led to poor performance. A positioning accuracy of 36.4 m was achieved using a contrastive learning approach by implementing a convolutional neural network (CNN)-based Siamese neural network that then compared imagery from the UAS camera to aerial satellite imagery. A similar approach was proposed by the authors of [197], who utilized a CNN trained with a large set of preexisting satellite imagery to predict location based on ground images from the UAS camera. The authors of [197] combated issues such as different lighting conditions, weather, and seasonal changes by building a large training dataset that contained images representative of all these conditions. The authors of [110] proposed using the You Only Look Once (YOLO) object recognition algorithm trained on aerial imagery. The trained YOLO model was then used to evaluate imagery from the onboard camera, demonstrating approximately 50-m position estimation resolution with a limited training dataset size; however, the approach performed poorly in regions sparse in features, such as a desert environment. Another interesting approach was proposed by the authors of [198], who built and trained an ML model with images representing each ground coordinate, like the proposed approach in [197]; however, they built the training dataset using Digital Elevation Map (DEM) images for each corresponding location instead of actual images. The goal was to then perform basic image processing on the image from the camera and then input that image into a CNN model for detection and classification. This approach was more immune to changing light conditions and seasonal changes than traditional image-based approaches.  
A key technical challenge with generalized vision approaches is the complexity of training the deep learning algorithms typically associated with these approaches. Consider the analysis of this issue presented in [110], which illustrates the dataset size requirements for a single global training dataset sufficiently representative of all locations on Earth. A globally applicable dataset that can achieve 10-m position accuracy resolution would require a minimum dataset size of approximately \(1.5\times 10^{15}\) images, assuming a single image representing each latitude and longitude coordinate separated by 10 m. These extremely large image training dataset sizes are problematic due to the corresponding computational requirements for model training. The authors of [110] proposed a region-based visual position estimation method with other sensors providing a coarse position estimation, in this case, an RF-based sensor utilizing cellular infrastructure, to mitigate this computational requirement. The resultant coarse position estimate was used to apply a region-specific visual method for precise position estimation selectively. These region-specific visual algorithms were trained against much smaller training datasets specific to a region, reducing computational complexity.