We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE/RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale products. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring.

Privacy-preserving biometric template protection schemes (BTPs) preserve biometric data by hiding biometric representations via a privacy-preserving mechanism (such as homomorphic encryption) and comparing the protected templates while conserving the recognition scores as in an embedding space. However, it is often tolerated to reveal these scores after performing a biometric comparison to gain efficiency and perform the score comparison directly on cleartext data. Through this work, we demonstrate that this cleartext score tolerance can lead to privacy breaches and bypass recognition systems, threatening those BTPs in the case of inner product-based facial template comparisons. We propose a template recovery attack that requires no training and a few random fake templates with their corresponding scores, from which we are able to recover the unprotected target template using the Lagrange multiplier optimization method. We evaluate our attack by verifying whether the recovered template is deemed similar to the target template held by recognition systems set to accept 0.1%, 0.01%, and 0.001% FMR. We estimate that between 60 to 165 revealed scores and fake templates can lead to a template recovery with a 100% success rate. We analyzed the impact of recovered templates by measuring the amount of gender information they contain, as well as their resemblance to the reconstructed images of their target templates.

We explore a new method to hide identity information in a facial image from face recognition (FR) systems, while only minimally changing the appearance of the image as perceived by humans. We train a decoder network that reverses the mapping of an FR system and use the dissimilarity score function of this FR system to teach the decoder to return images with as little identity information as possible, while using a visual loss to change the image as little as possible visually. We show that these obfuscation attacks are also successful when the FR system is unknown. We analyse the obfuscated images in latent space and show that our approach as well as an existing method can be easily circumvented by applying the same obfuscation method to the enrolled faces as to the probe images. We suggest an adaptation that can help prevent this circumvention.