Search Results (4)

Face anti-spoofing (FAS) has always been a hidden danger in network security, especially with the widespread application of facial recognition systems. However, some current FAS methods are not effective at detecting different forgery types and are prone to overfitting, which means they cannot effectively process unseen spoof types. Different loss functions significantly impact the classification effect based on the same feature extraction without considering the quality of the feature extraction. Therefore, it is necessary to find a loss function or a combination of different loss functions for spoofing detection tasks. This paper mainly aims to compare the effects of different loss functions or loss function combinations. We selected the Swin Transformer as the backbone of our training model to extract facial features to ensure the accuracy of the ablation experiment. For the application of loss functions, we adopted four classical loss functions: cross-entropy loss (CE loss), semi-hard triplet loss, L1 loss and focal loss. Finally, this work proposed combinations of Swin Transformers and different loss functions (pairs) to test through in-dataset experiments with some common FAS datasets (CelebA-Spoofing, CASIA-MFSD, Replay attack and OULU-NPU). We conclude that using a single loss function cannot produce the best results for the FAS task, and the best accuracy is obtained when applying triplet loss, cross-entropy loss and Smooth L1 loss as a loss combination. Full article

The remarkable development of deepfake models has facilitated the generation of fake content with various modalities, such as forged images, manipulated audio, and modified video with (or without) corresponding audio. However, many existing methods only analyze content with known and fixed modalities to identify deepfakes, which restricts their focus on intra-domain inconsistencies, and they fail to explore diverse modal and inter-domain hierarchical inconsistencies. In this work, we propose a novel unified neural network named MGDL-Net (Modal-Guided Domain Learning Network), which contains a spatial branch, a temporal branch, and a frequency branch. This diverse combination of branches endows our network with the ability to detect face-related input with flexible modalities and perceive both intra- and inter-domain inconsistencies, such as unimodal, bimodal, and trimodal modalities. To effectively and comprehensively capture the various inconsistencies, we propose implementing heterogeneous inconsistency learning (HIL) with a three-level joint extraction paradigm. In particular, HIL performs heterogeneous learning from spatial, temporal, and frequency perspectives to generate more generalized representations of forgery and eliminate the interference of static redundant information. Furthermore, a multi-modal deepfake dataset is also constructed. We have conducted extensive experiments, and our results have demonstrated that the proposed method can achieve an outstanding performance compared to that of numerous state-of-the-art methods, which implies that the cross-modal inconsistency learning we propose is beneficial for multi-modal face forgery detection. Full article

In recent years, some general cryptographic technologies have been widely used in network platforms related to the national economy and people’s livelihood, effectively curbing network security risks and maintaining the orderly operation and normal order of society. However, due to the fast development and considerable benefits of quantum computing, the classical cryptosystem faces serious security threats, so it is crucial to analyze and assess the anti-quantum computing ability of cryptographic algorithms under the quantum security model, to enhance or perfect the design defects of related algorithms. However, the current design and research of anti-quantum cryptography primarily focus on the cryptographic structure or working mode under the quantum security model, and there is a lack of quantum security analysis on instantiated cryptographic algorithms. This paper investigates the security of AES-OTR, one of the third-round algorithms in the CAESAR competition, under the Q2 model. The periodic functions of the associated data were constructed by forging the associated data according to the parallel and serial structure characteristics of the AES-OTR algorithm in processing the associated data, and the periodic functions of the associated data were constructed multiple times based on the Simon quantum algorithm. By using the collision pair, two collision forgery attacks on the AES-OTR algorithm can be successfully implemented, and the period s is obtained by solving with a probability close to 1. The attacks in this paper caused a significant threat to the security of the AES-OTR algorithm. Full article

In the era of rapid development of the Internet of things, deep learning, and communication technologies, social media has become an indispensable element. However, while enjoying the convenience brought by technological innovation, people are also facing the negative impact brought by them. Taking the users’ portraits of multimedia systems as examples, with the maturity of deep facial forgery technologies, personal portraits are facing malicious tampering and forgery, which pose a potential threat to personal privacy security and social impact. At present, the deep forgery detection methods are learning-based methods, which depend on the data to a certain extent. Enriching facial anti-spoofing datasets is an effective method to solve the above problem. Therefore, we propose an effective face swapping framework based on StyleGAN. We utilize the feature pyramid network to extract facial features and map them to the latent space of StyleGAN. In order to realize the transformation of identity, we explore the representation of identity information and propose an adaptive identity editing module. We design a simple and effective post-processing process to improve the authenticity of the images. Experiments show that our proposed method can effectively complete face swapping and provide high-quality data for deep forgery detection to ensure the security of multimedia systems. Full article