Siqi Gu

Ruiqi Zha, Zhichao Lian, Qianmu Li et al.

Most of previous deepfake detection researches bent their efforts to describe and discriminate artifacts in human perceptible ways, which leave a bias in the learned networks of ignoring some critical invariance features intra-class and underperforming the robustness of internet interference. Essentially, the target of deepfake detection problem is to represent natural faces and fake faces at the representation space discriminatively, and it reminds us whether we could optimize the feature extraction procedure at the representation space through constraining intra-class consistence and inter-class inconsistence to bring the intra-class representations close and push the inter-class representations apart? Therefore, inspired by contrastive representation learning, we tackle the deepfake detection problem through learning the invariant representations of both classes and propose a novel real-centric consistency learning method. We constraint the representation from both the sample level and the feature level. At the sample level, we take the procedure of deepfake synthesis into consideration and propose a novel forgery semantical-based pairing strategy to mine latent generation-related features. At the feature level, based on the centers of natural faces at the representation space, we design a hard positive mining and synthesizing method to simulate the potential marginal features. Besides, a hard negative fusion method is designed to improve the discrimination of negative marginal features with the help of supervised contrastive margin loss we developed. The effectiveness and robustness of the proposed method has been demonstrated through extensive experiments.

Sitong Liu, Zhichao Lian, Siqi Gu et al.

Deepfake detection methods based on convolutional neural networks (CNN) have demonstrated high accuracy. \textcolor{black}{However, these methods often suffer from decreased performance when faced with unknown forgery methods and common transformations such as resizing and blurring, resulting in deviations between training and testing domains.} This phenomenon, known as overfitting, poses a significant challenge. To address this issue, we propose a novel block shuffling regularization method. Firstly, our approach involves dividing the images into blocks and applying both intra-block and inter-block shuffling techniques. This process indirectly achieves weight-sharing across different dimensions. Secondly, we introduce an adversarial loss algorithm to mitigate the overfitting problem induced by the shuffling noise. Finally, we restore the spatial layout of the blocks to capture the semantic associations among them. Extensive experiments validate the effectiveness of our proposed method, which surpasses existing approaches in forgery face detection. Notably, our method exhibits excellent generalization capabilities, demonstrating robustness against cross-dataset evaluations and common image transformations. Especially our method can be easily integrated with various CNN models. Source code is available at \href{https://github.com/NoWindButRain/BlockShuffleLearning}{Github}.

Siqi Gu, Zhichao Lian

In this paper, a novel Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting (MFCC) is proposed, which utilizes an image fusion network architecture to fuse images from the visible and thermal infrared image, and a crowd counting network architecture to estimate the density map. The purpose of our framework is to fuse two modalities, including visible and thermal infrared images captured by drones in real-time, that exploit the complementary information to accurately count the dense population and then automatically guide the flight of the drone to supervise the dense crowd. To this end, we propose the unified multi-task learning framework for crowd counting for the first time and re-design the unified training loss functions to align the image fusion network and crowd counting network. We also design the Assisted Learning Module (ALM) to fuse the density map feature to the image fusion encoder process for learning the counting features. To improve the accuracy, we propose the Extensive Context Extraction Module (ECEM) that is based on a dense connection architecture to encode multi-receptive-fields contextual information and apply the Multi-domain Attention Block (MAB) for concerning the head region in the drone view. Finally, we apply the prediction map to automatically guide the drones to supervise the dense crowd. The experimental results on the DroneRGBT dataset show that, compared with the existing methods, ours has comparable results on objective evaluations and an easier training process.