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Found 7 papers, 0 papers with code
Towards Better Morphed Face Images without Ghosting Artifacts
Furthermore, we show that our approach does not impair the biometric quality, which is essential for high quality morphs.
From Explanations to Segmentation: Using Explainable AI for Image Segmentation
The new era of image segmentation leveraging the power of Deep Neural Nets (DNNs) comes with a price tag: to train a neural network for pixel-wise segmentation, a large amount of training samples has to be manually labeled on pixel-precision.
Explainable Artificial Intelligence (XAI)
Image Segmentation
+3
Automated Damage Inspection of Power Transmission Towers from UAV Images
Recently, the use of drones or helicopters for remote recording is increasing in the industry, sparing the technicians this perilous task.
Focused LRP: Explainable AI for Face Morphing Attack Detection
This evaluation framework is based on removing detected artifacts and analyzing the influence of these changes on the decision of the DNN.
Style Your Face Morph and Improve Your Face Morphing Attack Detector
A morphed face image is a synthetically created image that looks so similar to the faces of two subjects that both can use it for verification against a biometric verification system.
Reflection Analysis for Face Morphing Attack Detection
This map is compared with the highlights in the image that is suspected to be a fraud.
Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks
Artificial neural networks tend to learn only what they need for a task.
