DNN models have been widely applied to multimodal tasks, including cross-modal retrieval, image captioning, and visual question answering (VQA). While unimodal DNN models face serious security threats from backdoor attacks, backdoor attacks against multimodal DNN models are still underexplored. Current multimodal backdoor attack methods mainly inherit the ideas of unimodal backdoor attacks, making them difficult to adapt to complex multimodal tasks, which results in limited generalization and weak stealthiness of backdoor triggers. We propose a scene-aware multimodal bidirectional backdoor attack method (SABA). For different benign samples, this method first assesses the scene and selects adapted trigger samples based on the semantic understanding of the scene content. It constructs a scene-aware dynamic trigger generation mechanism, generating semantically constrained image and text triggers for different types of scenes, and inserts them into benign image or text samples to achieve a backdoor that is activated in one modality and effective in another modality. We evaluated the effectiveness of this method on two tasks: cross-modal retrieval and visual question answering (VQA). Compared to various SOTA methods for multimodal and unimodal backdoor attacks, SABA demonstrates strong effectiveness. Additionally, SABA can robustly evade existing backdoor defense strategies, posing a potential threat. Furthermore, we evaluate the stealthiness of the bidirectional backdoor attack method, and the comprehensive results are superior to those of three SOTA multimodal backdoor attack methods.

Network traffic anomaly detection is a critical means to detect network attacks, and plays a very important role in ensuring network security. However, existing network traffic anomaly detection methods rely on large-scale, well-labeled, class-balanced datasets, which are difficult to apply in practical application. Thus, this paper proposes a few shot network traffic anomaly detection method, called “SN-IDS”. “SN-IDS” includes a raw traffic encoding module and a convolution based siamese net(CSNet). The raw traffic encoding module converts the traffic into 3D images. The CSNet uses 3D convolution operations to extract the feature vectors of different traffic sessions from the 3D images and compares them in a metric way to detect anomalies. Experiments on the CICIDS2017 dataset show that the detection accuracy of our proposed method in the 5-shot scenario exceeds the current state-of-the-art methods.

Hyperspectral target detection is a crucial application that encompasses military, environmental, and civil needs. Target detection algorithms that have prior knowledge often assume a fixed laboratory target spectrum, which can differ significantly from the test image in the scene. This discrepancy can be attributed to various factors such as atmospheric conditions and sensor internal effects, resulting in decreased detection accuracy. To address this challenge, this article introduces a novel method for detecting hyperspectral image (HSI) targets with certain spatial information, referred to as the semantic transferred priori for hyperspectral target detection with spatial–spectral association (SSAD). Considering that the spatial textures of the HSI remain relatively constant compared to the spectral features, we propose to extract a unique and precise target spectrum from each image data via target detection in its spatial domain. Specifically, employing transfer learning, we designed a semantic segmentation network adapted for HSIs to discriminate the spatial areas of targets and then aggregated a customized target spectrum with those spectral pixels localized. With the extracted target spectrum, spectral dimensional target detection is performed subsequently by the constrained energy minimization (CEM) detector. The final detection results are obtained by combining an attention generator module to aggregate target features and deep stacked feature fusion (DSFF) module to hierarchically reduce the false alarm rate. Experiments demonstrate that our proposed method achieves higher detection accuracy and superior visual performance compared to the other benchmark methods.

Person re-identification (re-ID) which aims to recognize a pedestrian observed by non-overlapping cameras is a challenging task due to high variance between images from different viewpoints. Although remarkable progresses on research of re-ID had been obtained via leveraging the merits of deep learning framework through sufficient quantity training on a large amount of well labeled data, whereas, in real scenarios, re-ID generally suffers from lacking of well labeled training data. In this paper, we propose an attention-based model with attribute classification (AMAC) to facilitate a well trained model transferring across different data domains, which further enables an efficient cross-domain video-based person re-ID. Specifically, an attention-based sub-network is proposed for deep insight into the quality variations of local parts, hence, different local parts are cooperated with different weights to avoid the heavy occlusions or the cluttered background in datasets. Moreover, we introduce a transferred attribute classification sub-network to extract attribute-semantic features of any new target datasets without the requirement for new training attribute labels which are costly to annotate. Attribute-semantic features can be considered as valuable complementary information for person reidentification since they are robust to illumination varieties and different viewpoints across cameras. Due to the large gap between different datasets, we finetune each sub-network with pseudo labels on the target datasets respectively to strengthen the original model trained on other labeled datasets. Extensive comparable evaluations demonstrate the superiority of our AMAC in solving cross-domain person re- ID task on two benchmarks including PRID-2011 and iLIDS-VID.

We propose a novel analytical framework for evaluating the coverage performance of a millimeter wave (mmWave) cellular network where idle user equipments (UEs) act as relays. In this network, the base station (BS) adopts either the direct mode to transmit to the destination UE, or the relay mode if the direct mode fails, where the BS transmits to the relay UE and then the relay UE transmits to the destination UE. To address the drastic rotational movements of destination UEs in practice, we propose to adopt selection combining at destination UEs. New expression is derived for the signal-to-interference-plus noise ratio (SINR) coverage probability of the network. Using numerical results, we first demonstrate the accuracy of our new expression. Then we show that ignoring spatial correlation, which has been commonly adopted in the literature, leads to severe overestimation of the SINR coverage probability. Furthermore, we show that introducing relays into a mmWave cellular network vastly improves the coverage performance. In addition, we show that the optimal BS density maximizing the SINR coverage probability can be determined by using our analysis.

Polynomial evaluation is important across a wide range of application domains, so significant work has been done on accelerating its computation. The conventional algorithm, referred to as Horner's rule, involves the least number of steps but can lead to increased latency due to serial computation. Parallel evaluation algorithms such as Estrin's method have shorter latency than Horner's rule, but achieve this at the expense of large hardware overhead. This paper presents an efficient polynomial evaluation algorithm, which reforms the evaluation process to include an increased number of squaring steps. By using a squarer design that is more efficient than general multiplication, this can result in polynomial evaluation with a 57.9% latency reduction over Horner's rule and 14.6% over Estrin's method, while consuming less area than Horner's rule, when implemented on a Xilinx Virtex 6 FPGA. When applied in fixed point function evaluation, where precision requirements limit the rounding of operands, it still achieves a 52.4% performance gain compared to Horner's rule with only a 4% area overhead in evaluating 5th degree polynomials.

This paper presents an optimised high throughput architecture for integer squaring on FPGAs. The approach reduces the number of DSP blocks required compared to a standard multiplier. Previous work has proposed the tiling method for double precision squaring, using the least number of DSP blocks so far. However that approach incurs a large overhead in terms of look-up table (LUT) consumption and has a complex and irregular structure that is not suitable for higher word size. The architecture proposed in this paper can reduce DSP block usage by an equivalent amount to the tiling method while incurring a much lower LUT overhead: 21.8% fewer LUTs for a 53-bit squarer. The architecture is mapped to a Xilinx Virtex 6 FPGA and evaluated for a wide range of operand word sizes, demonstrating its scalability and efficiency.