Published Paper Details:

Security concerns have arisen in today's society as a result of using facial recognition systems that use deep learning model due to some feature being artificially manipulated. This paper revisits the question of how image manipulation in the form of social media filters for a facial feature or a facial expression impacts the recognition performance of models such as ResNet, MobileNet and InceptionV3. It was found that the recognition accuracy drops from 75% on unaltered images to 50% on altered images, thereby validating the observed weakness across the three architectures. Furthermore, a more advanced version VGG16 demonstrated to be more effective with 85% accuracy on altered images. This paper investigated these problems and proposed GRADCAM for heatmap analysis demonstrating why certain images with known resistive properties cannot be used to repulse such attacks, while also discussing possible countermeasures for protecting biometric systems.