Serverless computing, also known as Function-as-a-Service (FaaS), is widely used in modern applications. Function instances share the underlying physical infrastructure, which makes co-location attacks possible and leads to the leakage of sensitive information such as function activity states. Existing work has respective limitations in serverless scenarios because of incomplete detection coverage, long training time, and intrusion into the function's runtime environment. In this paper, we propose FaaSGuard, an obfuscation framework to protect function activity states in network side-channels and enhance privacy in serverless applications. To be specific, we design an adaptive obfuscation strategy selection mechanism to make FaaSGuard flexible. We design a traffic camouflage method to make obfuscated traffic indistinguishable from normal traffic, making FaaSGuard invisible. In order not to affect normal traffic, we propose a tag-based obfuscation mechanism to identify obfuscated packets. The preliminary evaluation results show that FaaSGuard can conceal function activity states with negligible resource overhead.

Underwater acoustic target recognition traditionally relies on feature engineering, wherein features are extracted through time-frequency transformations and fed into classifiers for target recognition. However, the noise distribution is uneven and lacks computational efficiency when using recognition models such as Transformers. To this end, this paper proposes a novel Multi-Scale Lightweight Adaptive Time Series Acoustic Network (MS-LATSANet), which is able to directly extract target discriminative information from raw signals without the need for complex feature engineering processing. Specifically, the network utilizes multi-scale time windows to capture different frequency components of the time series. In addition, MS-LATSANet employs modified discrete Fourier analysis with time domain background equalization to enhance feature representation, and uses adaptive thresholds to suppress noise. The network also introduces a global-local block to strengthen the understanding of temporal information. Extensive experimental results demonstrate that MS-LATSANet outperforms existing state-of-the-art models and exhibits stronger generalization performance under different signal-to-noise ratio scenarios.

This paper presents a novel zero-shot method for jointly denoising and enhancing real-word low-light images. The proposed method is independent of training data and noise distribution. Guided by illumination, we integrate denoising and enhancing processes seamlessly, enabling end-to-end training. Pairs of downsampled images are extracted from a single original low-light image and processed to preliminarily reduce noise. Based on the smoothness of illumination, near-authentic illumination can be estimated from the denoised low-light image. Specifically, the illumi-nation is constrained by the denoised image's brightness, uniformly amplifying pixels to raise overall brightness to normal-light level. We simultaneously restrict the illumi-nation by scaling each pixel of the denoised image based on its intensity, controlling the enhancement amplitude for different pixels. Applying the illumination to the original low-light image yields an adaptively enhanced reflection. This prevents under-enhancement and localized overexpo-sure. Notably, we concatenate the reflection with the illumi-nation, preserving their computational relationship, to ul-timately remove noise from the original low-light image in the form of reflection. This provides sufficient image infor-mation for the denoising procedure without changing the noise characteristics. Extensive experiments demonstrate that our method outperforms other state-of-the-art meth-ods. The source code is available at https://github.com/Doyle59217/ZeroIG.

Audio super-resolution aims to improve the quality of acoustic signals and is able to reconstruct corresponding high-resolution acoustic signals from low-resolution acoustic signals. However, since acoustic signals can be divided into two forms: time-domain acoustic waves or frequency-domain spectrograms, most existing research focuses on data enhancement in a single field, which can only obtain partial or local features of the audio signal, resulting in limitations of data analysis. Therefore, this paper proposes a time-frequency domain fusion enhanced audio super-resolution method to mine the complementarity of the two representations of acoustic signals. Specifically, we propose an end-to-end audio super-resolution network. Including the variational autoencoder based sound wave super-resolution module, U-Net-based Spectrogram Super-Resolution Module, and attention-based Time-Frequency Domain Fusion Module. The first two modules can generate more high-frequency and low-frequency components for audio respectively. As a critical component of our method, time-frequency domain fusion module performs weighted fusion on the above two outputs to obtain a super-resolution audio signal. Compared with other methods, experimental results on the VCTK and Piano datasets in natural scenes show that the time-frequency domain fusion audio super-resolution model has a state-of-the-art bandwidth expansion effect. Furthermore, we perform super-resolution on the ShipsEar dataset containing underwater acoustic signals. The super-resolution results are used to test ship target recognition, and and the accuracy is improved by 12.66%. Therefore, the proposed super-resolution method has excellent signal enhancement effect and generalization ability.

Large amounts of data are widely stored in cyberspace. Not only can they bring much convenience to people’s lives and work, but they can also assist the work in the information security field, such as microexpression recognition and sentiment analysis in the criminal investigation. Thus, it is of great significance to recognize and analyze the sentiment information, which is usually described by different modalities. Due to the correlation among different modalities data, multimodal can provide more comprehensive and robust information than unimodal in data analysis tasks. The complementary information from different modalities can be obtained by multimodal fusion methods. These approaches can process multimodal data through fusion algorithms and ensure the accuracy of the information used for subsequent classification or prediction tasks. In this study, a two-level multimodal fusion (TlMF) method with both data-level and decision-level fusion is proposed to achieve the sentiment analysis task. In the data-level fusion stage, a tensor fusion network is utilized to obtain the text-audio and text-video embeddings by fusing the text with audio and video features, respectively. During the decision-level fusion stage, the soft fusion method is adopted to fuse the classification or prediction results of the upstream classifiers, so that the final classification or prediction results can be as accurate as possible. The proposed method is tested on the CMU-MOSI, CMU-MOSEI, and IEMOCAP datasets, and the empirical results and ablation studies confirm the effectiveness of TlMF in capturing useful information from all the test modalities.