As deepfake technology has become increasingly sophisticated and accessible, making it easier for individuals with malicious intent to create convincing fake content, which has raised considerable concern in the multimedia and computer vision community. Despite significant advances in deepfake video detection, most existing methods mainly focused on model architecture and training processes with little focus on data perspectives. In this paper, we argue that data quality has become the main bottleneck of current research. To be specific, in the pre-training phase, the domain shift between pre-training and target datasets may lead to poor generalization ability. Meanwhile, in the training phase, the low fidelity of the existing datasets leads to detectors relying on specific low-level visual artifacts or inconsistency. To overcome the shortcomings, (1). In the pre-training phase, pre-train our model on high-quality facial videos by utilizing data-efficient reconstruction-based self-supervised learning to solve domain shift. (2). In the training phase, we develop a novel spatio-temporal generator that can synthesize various high-quality "fake" videos in large quantities at a low cost, which enables our model to learn more general spatio-temporal representations in a self-supervised manner. (3). Additinally, to take full advantage of synthetic "fake" videos, we adopt diversity losses at both frame and video levels to explore the diversity of clues in "fake" videos. Our proposed framework is data-efficient and does not require any real-world deepfake videos. Extensive experiments demonstrate that our method significantly improves the generalization capability. Particularly on the most challenging CDF and DFDC datasets, our method outperforms the baselines by 8.88% and 7.73% points, respectively.

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100 percent, and the semantic segmentation based attacks have a success rate over 90 percent among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.

Adversarial examples (AEs) against deep neural networks (DNNs) raise wide concerns about the robustness of DNNs. Existing detection mechanisms are often limited to a given attack algorithm. Therefore, it is highly desirable to develop a robust detection approach that remains effective for a large group of attack algorithms. In addition, most of the existing defences only perform well for small images (e.g. MNIST and Canadian institute for advanced research (CIFAR)) rather than large images (e.g. ImageNet). In this paper, the authors propose a robust and effective defence method for analysing the sensitivity of various AEs, especially in a much harder case (large images). Their method first creates a feature map from the input space to the new feature space, by utilising 19 different feature mapping methods. Then, a detector is learned with the machine‐learning algorithm to recognise the unique distribution of AEs. Their extensive evaluations on their proposed detector show that their detector can achieve: (i) low false‐positive rate (<1%), (ii) high true‐positive rate (higher than 98%), (iii) low overhead (<0.1 s per input), and (iv) good robustness (work well across different learning models, attack algorithms, and parameters), which demonstrate the efficacy of the proposed detector in practise.