Spatial-Frequency Feature Fusion Network for Lightweight and Arbitrary-Sized JPEG Steganalysis

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-09-17 DOI:10.1109/LSP.2024.3462174
Xulong Liu;Weixiang Li;Kaiqing Lin;Bin Li
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Abstract

Current deep learning-based JPEG image steganalysis methods typically rely on decompressed pixels for steganalytic feature extraction, without fully leveraging the inherent information in JPEG images. Additionally, they often face limitations such as large parameter counts and restricted image sizes for detection. In this letter, we propose a spatial-frequency feature fusion network (SF3Net) for lightweight and arbitrary-sized JPEG steganalysis. SF3Net introduces a PReLU activation function and a multi-view convolutional module to capture refined residual features from decompressed pixels, while also integrating original DCT coefficients and quantization tables to extract additional modal features. The spatial-frequency multi-modality features are then fused using a coordinate attention mechanism. And a patch splitting scheme is designed to be compatible with any feature resolution, enabling the detection of arbitrary-sized images with a Swin Transformer block. Experimental results demonstrate that SF3Net outperforms existing methods in detecting both fixed-sized and arbitrary-sized images, while significantly reducing the number of parameters.
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用于轻量级和任意大小 JPEG 隐藏分析的空间-频率特性融合网络
目前基于深度学习的 JPEG 图像隐写分析方法通常依赖于解压缩像素进行隐写特征提取,无法充分利用 JPEG 图像中的固有信息。此外,这些方法还经常面临参数数量大、检测图像大小受限等限制。在这封信中,我们提出了一种空间-频率特性融合网络(SF3Net),用于轻量级和任意大小的 JPEG 隐藏分析。SF3Net 引入了一个 PReLU 激活函数和一个多视图卷积模块,以捕捉解压缩像素的细化残余特征,同时还整合了原始 DCT 系数和量化表,以提取额外的模态特征。然后利用坐标注意机制融合空间-频率多模态特征。此外,还设计了一种补丁分割方案,可与任何特征分辨率兼容,从而使用 Swin 变换器块检测任意大小的图像。实验结果表明,在检测固定尺寸和任意尺寸图像方面,SF3Net 的性能均优于现有方法,同时大大减少了参数数量。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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