The usual active learning is to sample unlabeled set by designing efficient sample information evaluation algorithms. However, information redundancy between candidate sets is often overlooked. This can cause similar data to be labeled repeatedly, producing ineffective gains for the model. In this paper, we proposed an Unsupervised Redundant Feature Elimination Active Learning module (URFEAL), which utilizes the information feature coincidence of the unlabeled set to eliminate information redundant data, thus guaranteeing the validity of each candidate data. URFEAL consists of feature clusterer and eliminator. The feature clusterer computes class boundaries based on feature densities to discretize each class of the candidate set, and the eliminator judges data similarity by overlapping degree to eliminate redundant data features. Furthermore, we propose an anti-noise sampling strategy Outlier Feature Elimination (OFE) in URFEAL to filter mislabeled sets for relabeling in the data sampling stage. We extensively evaluate our method by image classification and perform experimental validation on CIFAR-10, CIFAR-100 and CALTECH-101. The experimental results show that the improvements we make are especially significant for most existing active learning algorithms in the low data stage, which demonstrates the effectiveness and generality of URFEAL.

Lots of Deepfake videos are circulating on the Internet, which not only damages the personal rights of the forged individual, but also pollutes the web environment. What’s worse, it may trigger public opinion and endanger national security. Therefore, it is urgent to fight deep forgery. Most of the current forgery detection algorithms are based on convolutional neural networks to learn the feature differences between forged and real frames from big data. In this paper, from the perspective of image generation, we simulate the forgery process based on image generation and explore possible trace of forgery. We propose a multi-scale self-texture attention Generative Network(MSTA-Net) to track the potential texture trace in image generation process and eliminate the interference of deep forgery post-processing. Firstly, a generator with encoder-decoder is to disassemble images and performed trace generation, then we merge the generated trace image and the original map, which is input into the classifier with Resnet as the backbone. Secondly, the self-texture attention mechanism(STA) is proposed as the skip connection between the encoder and the decoder, which significantly enhances the texture characteristics in the image disassembly process and assists the generation of texture trace. Finally, we propose a loss function called Prob-tuple loss restricted by classification probability to amend the generation of forgery trace directly. To verify the performance of the MSTA-Net, we design different experiments to verify the feasibility and advancement of the method. Experimental results show that the proposed method performs well on deep forged databases represented by FaceForensics++, Celeb-DF, Deeperforensics and DFDC, and some results are reaching the state-of-the-art.

Most of the current blind stereoscopic image quality assessment (SIQA) algorithms cannot show reliable accuracy. One reason is that they do not have the deep architectures and the other reason is that they are designed on the relatively weak biological basis, compared with findings on human visual system (HVS). In this paper, we propose a Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) based on the whole visual perception route from eyes to the frontal lobe, and especially focus on edge and color signal processing in retinal ganglion cells (RGC) and lateral geniculate nucleus (LGN). Furthermore, to model the complex and deep structure of the visual cortex, Segmented Stacked Auto-encoder (S-SAE) is used, which has not utilized for SIQA before. The utilization of the S-SAE complements weakness of deep learning-based SIQA metrics that require a very long training time. Experiments are conducted on popular SIQA databases, and the superiority of DECOSINE in terms of prediction accuracy and monotonicity is proved. The experimental results show that our model about the whole visual perception route and utilization of S-SAE are effective for SIQA.

Most previous 2-D and 3-D image quality evaluators were based on shallow architectures. Their shallow architectures cannot model phenomenon occurring in human visual systems sufficiently. Disparities between left and right views have been importantly used for 3-D image quality assessment (IQA), but single disparity, depth, or cyclopean maps made from the disparities cannot thoroughly reflect the depth sense. In this paper, we propose a blind stereoscopic image quality evaluator using stacked auto-encoders (SAE). The proposed method is based on two theories on initial stages of stereopsis. One is cyclopean channel theory and the other is binocular summation/difference channels theory. Especially, a cyclopean image that models the former theory is computed to consider binocular suppression, whereas summation and difference images that model the latter one are utilized to treat the depth sense. We train three SAEs for quality-aware features from the three images in an unsupervised manner. Through the SAEs, the features are transformed into more meaningful features, and they are used to train two regressors. The regressors are used to obtain a final predicted score. Experimental results conducted on popular 3-D IQA databases prove that the proposed algorithm outperforms state-of-the-art 3-D IQA methods.