Four distinct GAN-based I2I translation techniques are employed for the synthesis of biometric finger vein presentation attack instrument (PAI) samples corresponding to three public presentation attack datasets. The PAD training using these synthetic PAI samples reveals weaknesses for a small share of settings (in terms of datasets and GAN types). Removing the GAN model fingerprints (with three technical variants) from the synthetic data is not resolving these problematic PAD results, in contrary, this strategy creates more problematic results than it was intended to resolve. Finally, we show that PAI samples generated with different GAN types can be easily discriminated, even when using identical GAN types but only different parameter setups the resulting synthetic data can still be differentiated. This indicates that the present GAN model fingerprints are stronger than often believed, eventually caused by the significant redundancy present in our biometric datasets as compared to natural data as typically used in GAN fingerprint assessments. Overall, in the generation of synthetic PAI samples, CycleGAN as well as StarGANv2 generated specimens turn out to be highly useful to train finger vein PAD systems.

This study investigates the integration of low-cost unoccupied aerial vehicles (UAVs) and community engagement in permafrost monitoring. Utilizing novel UAV flight patterns and crowdsourced data analysis, including a Convolutional Neural Network (CNN), the study enhances digital surface models (DSMs) for identifying ice-wedge polygons. Conducted in rapidly changing Arctic regions, it demonstrates an overall accuracy of 74.56% in feature detection. This approach offers improved resolution in environmental monitoring and suggests potential for broader application and rapid disaster response through community-sourced scientific analysis and consumer UAVs.

This work investigates the possibility to conduct a forensic discrimination of decoded versions of 10 different lossy image compression file formats, including 4 ISO/IEC still image compression standards (JPEG, JPEG 2000, JPEG XR, JPEG XL) and 4 video-coding related image compression schemes (AVIF, HEIC, BPG, WEBP). We have found that a proper compression artefact discrimination can be achieved across different compression ratios by fine-tuning a standard ResNet-18 model using a variety of different file sizes in training. Classification accuracy is almost perfect for low quality image data (as compression artefacts are strong), while the 10-class discrimination accuracy is slightly beyond 85% for high quality imagery which can be considered almost visually lossless. Observed mis-classifications are mostly along the lines of expectations due to algorithmic differences and similarities (block-size, transform type, etc.), only JPEG 2000 exhibits some unexpected artefact similarities to JPEG XR when the photo overlap transform is being employed.

Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited.