Quantifying Noise of Dynamic Vision Sensor

Dynamic visual sensors (DVS) are characterized by a large amount of background activity (BA) noise, which it is mixed with the original (cleaned) sensor signal. The dynamic nature of the signal and the absence in practical application of the ground truth, it clearly makes difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. In this letter, a new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA). The proposed technique can be used to address an existing DVS issues, which is how to quantitatively characterised noise and signal without ground truth, and how to derive an optimal denoising filter parameters. The solution of the latter problem is demonstrated for the popular real moving-car dataset.

Adversarially robust deepfake media detection using fused convolutional neural network predictions

Deepfakes are synthetically generated images, videos or audios, which fraudsters use to manipulate legitimate information. Current deepfake detection systems struggle against unseen data. To address this, we employ three different deep Convolutional Neural Network (CNN) models, (1) VGG16, (2) InceptionV3, and (3) XceptionNet to classify fake and real images extracted from videos. We also constructed a fusion of the deep CNN models to improve the robustness and generalisation capability. The proposed technique outperforms state-of-the-art models with 96.5% accuracy, when tested on publicly available DeepFake Detection Challenge (DFDC) test data, comprising of 400 videos. The fusion model achieves 99% accuracy on lower quality DeepFake-TIMIT dataset videos and 91.88% on higher quality DeepFake-TIMIT videos. In addition to this, we prove that prediction fusion is more robust against adversarial attacks. If one model is compromised by an adversarial attack, the prediction fusion does not let it affect the overall classification.

High Dynamic Range Imaging Technology

In this lecture note, we describe high dynamic range (HDR) imaging systems; such systems are able to represent luminances of much larger brightness and, typically, also a larger range of colors than conventional standard dynamic range (SDR) imaging systems. The larger luminance range greatly improve the overall quality of visual content, making it appears much more realistic and appealing to observers. HDR is one of the key technologies of the future imaging pipeline, which will change the way the digital visual content is represented and manipulated today.