To enhance information security during transmission over public channels, images are frequently employed for binary data hiding. Nonetheless, data are vulnerable to distortion due to Joint Photographic Experts Group (JPEG) compression, leading to challenges in recovering the original binary data. Addressing this issue, this paper introduces a pioneering method for binary data hiding that leverages a combined spatial and channel attention Transformer, termed SCFformer, to withstand JPEG compression. This method employs a novel discrete cosine transform (DCT) quantization truncation mechanism during the hiding phase to bolster the stego image’s resistance to JPEG compression, using spatial and channel attention to conceal information in less perceptible areas, thereby enhancing the model’s resistance to steganalysis. In the extraction phase, the DCT quantization minimizes secret image loss during compression, facilitating easier information retrieval. The incorporation of scalable modules adds flexibility, allowing for variable-capacity data hiding. Experimental findings validate the high security, large capacity, and high flexibility of our scheme, alongside a marked improvement in binary data recovery post-JPEG compression, underscoring our method’s leading-edge performance.

Recent research has shown that deep learning networks are vulnerable to adversarial samples. Although there has been great progress in the study of adversarial attacks on images, there is relatively little research on adversarial attacks in the video domain, especially on intrinsic factors of videos, such as motion blur. In this paper, we devise a novel Grad-Weighted based One-step Motion Blur Attack (GWO-MBA) and a Discrete-Fusion based Progressive Motion Blur Attack (DFP-MBA) for video recognition, starting from the idea of integrating global adversarial attacks and adversarial patch attacks. Concretely, we use gradient maps to filter and weighted fusion motion blur (termed GWO-MBA) to achieve the attack that matches the motion information in the context of the video. In order to make the generated motion blur attack perturbations more natural and improve the attack success rate, we further introduce a progressive decomposition motion blur strategy (termed DFP-MBA) to progressively fuse more realistic discrete motion blurs. Besides, we propose an Aggressive Motion Blur Generation (AMBG), which generates natural motion blur based on the video context and has a better attack effect. The extensive experiments, on the HMDB-51 and UCF-101 datasets, demonstrate the effectiveness and superiority of our proposed attack method. In addition, the attack effectiveness of the mainstream denoising defense model and the deblur model further validates the robustness of our attack method.

Adversarial attacks have been extensively studied in the image field. In recent years, research has shown that video recognition models are also vulnerable to adversarial examples. However, most studies about adversarial attacks for video models have focused on perturbation-based methods, while patch-based black-box attacks have received less attention. Despite the excellent performance of perturbation-based attacks, these attacks are impractical for real-world implementation. Most existing patch-based black-box attacks require occluding larger areas and performing more queries to the target model. In this paper, we propose a hard-sample style guided patch attack with reinforcement learning (RL) enhanced motion patterns for video recognition (HSPA). Specifically, we utilize the style features of video hard samples and transfer their multi-dimensional style features to images to obtain a texture patch set. Then we use reinforcement learning to locate the patch coordinates and obtain a specific adversarial motion pattern of the patch to successfully perform an effective attack on a video recognition model in both the spatial and temporal dimensions. Our experiments on three widely-used video action recognition models (C3D, LRCN, and TDN) and two mainstream datasets (UCF-101 and HMDB-51) demonstrate the superior performance of our method compared to other state-of-the-art approaches.

In recent years, the application of video Internet of Things (IoT) in various cities and public places has brought unprecedented opportunities to the security field and achieved great success. However, the latest research shows that video recognition models are also vulnerable to adversarial examples, but adversarial examples based on physical attacks are easily detected by humans, making it difficult to pass human review. To address this problem, in this article, we propose to introduce a novel multigranular spatiotemporal attention network (MSANet), which can attack the video action recognition models imperceptibly. Specifically, to exploit video motion information more effectively and to reduce the detectability of attack perturbations, we design a multiplexed spatiotemporal attention module to select and enhance spatial regions and temporal frames at coarse-grained and fine-grained levels, respectively, thus maintaining a certain degree of smoothness while reducing the perturbation size and avoiding attacking overfitting. In addition, our proposed MSANet achieves imperceptible perturbations to video sequences through alternate iterative optimization combined with the PGD attack mechanism. extended experimental results on two different models (e.g., TDN and TSM) and two widely used data sets [HMDB-51 (Kuehne et al., 2011) and UCF-101 (Soomro et al., 2012)], compared to the state-of-the-art model, demonstrate the effectiveness of our devised video action recognition attack approach.

In recent years, Internet of Things (IoT) has made rapid development, and IoT devices are developing towards intelligence. IoT terminal devices represented by surveillance cameras play an irreplaceable role in modern society, most of them are integrated with video action recognition and other intelligent functions. However, their performance is somewhat affected by the limitation of computing resources of IoT terminal devices and the lack of long-range non-linear temporal relation modeling and reverse motion information modeling. To address this urgent problem, we introduce a startling Temporal Transformer Network with Self-supervision (TTSN). Our high-performance TTSN mainly consists of a temporal transformer module and a temporal sequence self-supervision module. Concisely speaking, we utilize the efficient temporal transformer module to model the non-linear temporal dependencies among non-local frames, which significantly enhances complex motion feature representations. The temporal sequence self-supervision module we employ unprecedentedly adopts the streamlined strategy of “random batch random channel” to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model. Extensive experiments on three widely used datasets (HMDB51, UCF101, and Something-something V1) have conclusively demonstrated that our proposed TTSN is promising as it successfully achieves state-of-the-art performance for video action recognition. Our TTSN provides the possibility for its application in IoT scenarios due to its computational complexity and high performance. With the rapid development of the Internet of Things (IoT), more and more data is being disseminated in the form of video, which also puts new requirements on the understanding and modeling of video data. In recent years, 2D Convolutional Networks-based video action recognition has encouragingly gained wide popularity; However, constrained by the lack of long-range non-linear temporal relation modeling and reverse motion information modeling, the performance of existing models is, therefore, undercut seriously. To address this urgent problem, we introduce a startling Temporal Transformer Network with Self-supervision (TTSN). Our high-performance TTSN mainly consists of a temporal transformer module and a temporal sequence self-supervision module. Concisely speaking, we utilize the efficient temporal transformer module to model the non-linear temporal dependencies among non-local frames, which significantly enhances complex motion feature representations. The temporal sequence self-supervision module we employ unprecedentedly adopts the streamlined strategy of “random batch random channel” to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model. Extensive experiments on three widely used datasets (HMDB51, UCF101, and Something-something V1) have conclusively demonstrated that our proposed TTSN is promising as it successfully achieves state-of-the-art performance for video action recognition. As a result, our work provides new attention and self-supervised algorithm for processing video data in IoT.

With the development of deep neural networks, video action recognition has gradually become a research hotspot in recent years. However, the additional temporal dimension in video makes this task very challenging. In this paper, we propose a novel Spatio-Temporal Self-Supervision enhanced Transformer Networks (STTNet) for video action recognition, which mainly consists of Self-Supervised SpatioTemporal Representation Learning module and Transformer based Spatio-Temporal Aggregator module. Concretely, our STTNet can adaptively encode the spatial and temporal enhanced key features, which are respectively learned through the Temporal and Spatial Self-Supervised sub-module using the unlabeled video data, in a nonlinear and nonlocal manner via the Transformer based SpatioTemporal Aggrega-tor. The extensive experiments on three widely used datasets (HMDB51, UCF101 and Something-Something V1) demon-strate that our proposed STTNet can achieve the state-of-the-art performance. Code is available at https://github.com/ICME2022/STTNet.

2D convolutional neural network, due to its low computational complexity and fast recognition speed, has attracted more and more attention from researchers in the field of video action recognition. Temporal shift and temporal differential, have made tremendous progress, but the lack of crucial spatiotemporal attention mechanism has led to huge performance loss. To address this issue, we propose a Temporal-guided Multiattention Network (TMN), which fully excavate and fuse spatio-temporal attention information for effective video action recognition. Concretely, the multi-attention module squeezes and expands spatio-temporal features to achieve weighting of corresponding regions for video in spatio-temporal dimensions, while the adaptive temporal guidance module imports temporal guiding signal to the spatial attention and re-weight the global temporal attention to accomplish the accurate temporal modeling. Extensive experiments and analyses show that our proposed temporal-guided multiattention network can achieve state-of-the-art promising video action recognition performance on the widely used benchmarks (HMDB51, UCF101 and Something-Something V1).