The International Fingerprint Liveness Detection Competition is an international biennial competition open to academia and industry with the aim to assess and report advances in Fingerprint Presentation Attack Detection. The proposed "Liveness Detection in Action" and "Fingerprint representation" challenges were aimed to evaluate the impact of a PAD embedded into a verification system, and the effectiveness and compactness of feature sets for mobile applications. Furthermore, we experimented a new spoof fabrication method that has particularly affected the final results. Twenty-three algorithms were submitted to the competition, the maximum number ever achieved by LivDet.
9Field soil pore water monitoring was applied in a highly heavy-metal contaminated area in SW 10 Sardinia, Italy, as a direct, realistic measure of heavy metal mobility. The main chemistry of pore 11 waters well reflects the local characteristics of soils, ranging from Ca-SO 4 to (Ca)Mg-HCO 3 to 12 Ca(Na)-SO 4 (Cl), with a wide range of conductivity. The mobility of Zn and Pb is apparently 13 controlled by equilibrium with minerals such as hydrozincite or smithsonite, and cerussite, 14 respectively. These results allow a correct estimate of the actual environmental risk associated with 15 the presence of heavy metals in soils, and may serve as a supporting tool for phytoremediation 16 planning. 17 18
Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable of working in an “in the wild” scenario; (ii) creating a method capable of reconstructing original images from Deepfakes. Real images from CelebA and FFHQ and Deepfake images created by StarGAN, StarGAN-v2, StyleGAN, StyleGAN2, AttGAN and GDWCT were collected for the competition. The winning teams were chosen with respect to the highest classification accuracy value (Task I) and “minimum average distance to Manhattan” (Task II). Deep Learning algorithms, particularly those based on the EfficientNet architecture, achieved the best results in Task I. No winners were proclaimed for Task II. A detailed discussion of teams’ proposed methods with corresponding ranking is presented in this paper.
When deepfakes are widespread on chatting platforms, they are expected to be subject to heavy resizing and compressing steps. In this paper, we present a tensor-based representation of compressed and resized images. Tensor embeds DCT features computed on multi-scaled and multi-compressed versions of the input facial image. Moreover, a custom deeparchitecture is designed and trained on the proposed representation. Experimental results show its pros and cons with respect to state-of-the-art methods.
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