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Manipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of state-of-the-art video CNNs including 3D ResNet, 3D ResNeXt, and I3D in detecting manipulated videos. In addition, and toward a more robust detection, we investigate the effectiveness of attention mechanisms in this context. Such mechanisms are introduced in CNN architectures in order to ensure that robust features are being learnt. We test two attention mechanisms, namely SE-block and Non-local networks. We present related experimental results on videos tampered by four manipulation techniques, as included in the FaceForensics++ dataset. We investigate three scenarios, where the networks are trained to detect (a) all manipulated videos, (b) each manipulation technique individually, as well as (c) the veracity of videos pertaining to manipulation techniques not included in the train set.

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Performance of state-of-the-art fingerprint denoising model on poor quality fingerprints degrades due to crossdomain shift observed between training and testing domains. To address this limitation, we present a cross-domain consistent fingerprint denoising model, which ensures that the output of two fingerprint images with the same ridge structure, however varying contrast and ridge-valley clarity should be similar. Results indicate that the proposed CDC-GAN outperforms state-of-the-art fingerprint denoising algorithms on challenging publicly available poor quality fingerprint databases.

The state-of-the-art fingerprint matching systems achieve high accuracy on good quality fingerprints. However, degraded fingerprints obtained due to poor skin conditions of subjects or fingerprints obtained around a crime scene often have noisy background and poor ridge structure. Such degraded fingerprints pose problem for the existing fingerprint recognition systems. This paper presents a fingerprint restoration model for a poor quality fingerprint that reconstructs a binarized fingerprint image with an improved ridge structure. In particular, we demonstrate the effectiveness of channel refinement in fingerprint restoration. The state-of-the-art channel refinement mechanisms, such as Squeeze and Excitation (SE) block, in general, create SEblock introduce redundancy among channel weights and degrade the performance of fingerprint enhancement models. We present a lightweight attention mechanism

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