A prevalent video manipulation technique involves upsampling the bitrate parameter without modifying the underlying video content, often implemented under the pretext of improving viewer appeal and monetization potential. Such deceptive practices, which substitute fake bitrate specifications for genuine ones, mislead audiences and platforms while directly infringing copyright protections, constituting a form of manipulation that forensic experts classify as fake bitrate video manipulation. Addressing the issue of detecting fake bitrate Versatile Video Coding (VVC) videos, an algorithm based on hierarchical feature mapping in the coding domain is proposed. We first analyze the Coding Unit (CU) partitioning and the deblocking filtering of fake bitrate VVC videos during multiple encoding processes. Then, CU partitioning and deblocking decision-mode information in the coding domain are extracted during the decoding process. By combining the positional information, the CU Difference feature map (FCD) and the Deblocking Filtering Difference feature map (FDD) are obtained through a hierarchical mapping of the encoding feature, which are further enhanced by calculating the eccentric covariance matrix. After that, the enhanced feature maps are fed into a Dual-branch Difference Perception (DDP) module to obtain frame-level detection results. By comparing with existing algorithms, the experimental results demonstrate that the proposed algorithm achieves superior detection accuracy in different scenarios, validating its effectiveness and robustness.

The growing adoption of 3D point cloud in applications like autonomous driving has heightened concerns about their vulnerability to adversarial attacks. Existing defense methods face two fundamental challenges: ineffective detection of imperceptible adversarial examples and poor restoration of severely distorted point cloud. In this paper, we present ADDR, an end-to-end defense framework that integratesBinary Geometric Feature Anomaly Detection (BGFAD)andDistorted point cloud Restoration (DPCR). BGFAD employs a dual threshold mechanism combining global distance statistics and local curvature analysis to detect both substantial and imperceptible adversarial perturbations. DPCR leverages attention enhanced feature encoding to reconstruct missing geometric structures while preserving semantic integrity through bidirectional Chamfer loss optimization. Our framework uniquely bridges traditional geometric priors with deep learning mechanisms, achieving attack-agnostic defense without classifier retraining. Extensive experiments on ModelNet40, ShapeNet and ScanObjectNN datasets demonstrate state-of-the-art performance, with about 12% higher robustness against structural attacks and 6× better restoration fidelity than existing methods. ADDR maintains real-time processing capabilities while reducing adversarial success rates to <5% across diverse attacks. The code is available at https://github.com/whwh456/ADDR.

Abstract Detection of computer-generated (CG) images can reveal the authenticity and originality of digital images. However, recent cutting-edge image generation methods make it very difficult to distinguish CG images from natural photographs. In this paper, a novel method based on color patterns and enhanced texture learning is proposed to tackle this problem. We designed and implemented the backbone network with a separation-fusion learning strategy by constructing a multi-branch neural network. The luminance and chrominance patterns in dual-color spaces (RGB and YCbCr) are leveraged to achieve a robust representation of image differences. A channel-spatial attention module and a global texture enhancement module are also integrated into a backbone network to enhance the learning of inherent traces. Experiments on several commonly used benchmark datasets and a newly constructed dataset with more realistic and diverse images demonstrate that the proposed algorithm outperforms state-of-the-art competitors by a large margin.

Federated learning (FL) can complete collaborative model training without transferring local data, which can greatly improve the training efficiency. However, FL is susceptible data and model backdoor attacks. To address data backdoor attack, in this article, we propose a defense method named TSF. TSF transforms data from time domain to frequency domain and subsequently designs a low-pass filter to mitigate the impact of high-frequency signals introduced by backdoor samples. Additionally, we undergo homomorphic encryption on local updates to prevent the server from inferring user’s data. We also introduce a defense method against model backdoor attack named ciphertext field similarity detect differential privacy (CFSD-DP). CFSD-DP screens malicious updates using cosine similarity detection in the ciphertext domain. It perturbs the global model using differential privacy mechanism to mitigate the impact of model backdoor attack. It can effectively detect malicious updates and safeguard the privacy of the global model. Experimental results show that the proposed TSF and CFSD-DP have 73.8% degradation in backdoor accuracy while only 3% impact on the main task accuracy compared with state-of-the-art schemes. Code is available athttps://github.com/whwh456/TSF.