多尺度感知与边界引导的图像篡改检测方法

doi:10.13229/j.cnki.jdxbgxb.20231027

Abstract

Abstract:

Aiming at the problems of traditional image manipulation detection methods， such as fuzzy boundaries， single scale of extracted features， and ignoring background information， this paper proposes an image manipulation detection method based on multi-scale context-aware and boundary-guided. First， spatial details and base features of manipulated images are extracted using an improved pyramid vision transformer. Second， information related to the edge of the falsified region is explored by an edge context-aware module to generate an edge prediction map. Again， the edge guidance module is utilized to highlight the key channels in the extracted features and reduce the interference of redundant channels. Then， the rich contextual information of the manipulated region is learned from multiple sensory fields through the multi-scale context-aware module. Finally， the feature fusion module is utilized to accurately segment the manipulated region by focusing alternately on the foreground and background of the manipulated images. Comparing this paper's method quantitatively and qualitatively on five commonly used public image manipulation detection datasets， the experimental results show that this paper's method can effectively detect manipulated regions and outperforms other methods.

Key words: computer application, image manipulation detection, multi-scale context-aware, boundary guidance

CLC Number:

TP391

Hai-peng CHEN,Shi-bo ZHANG,Ying-da LYU. Multi⁃scale context⁃aware and boundary⁃guided image manipulation detection method[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 2114-2121.

Figures/Tables 10

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数据集	规模		类别			后处理操作
数据集	训练集	测试集	拼接	复制-粘贴	移除	后处理操作
CASIA^［13］	5 123	921	√		√	√
NIST^［14］	404	160	√	√	√	√
Columbia^［15］	—	180	√
COVER^［16］	75	25		√		√
IMD2020^［17］	1 610	400				√

方法	CASIA		NIST		Columbia		COVER		IMD2020		Mean
方法	AUC	F₁	AUC	F₁	AUC	F₁	AUC	F₁	AUC	F₁	AUC	F₁
ManTra^［18］	0.796	0.267	0.959	0.638	0.736	0.243	0.777	0.283	0.773	0.249	0.808	0.336
SPAN^［19］	0.709	0.213	0.779	0.252	0.741	0.463	0.791	0.325	—	—	0.755	0.313
MVSS^［20］	0.847	0.318	0.981	0.768	0.808	0.417	0.808	0.284	0.802	0.396	0.849	0.437
GSRNet^［8］	0.836	0.340	0.967	0.640	0.900	0.433	0.788	0.218	—	—	0.873	0.408
DenseFCN^［21］	0.809	0.203	0.979	0.812	0.761	0.257	0.754	0.185	0.715	0.272	0.804	0.346
LocateNet^［22］	0.754	0.273	0.986	0.738	0.718	0.411	0.813	0.282	—	—	0.818	0.426
EMTNet^［10］	0.856	0.459	0.987	0.825	0.832	0.561	0.812	0.353	—	—	0.872	0.550
Ours	0.848	0.647	0.965	0.898	0.770	0.609	0.776	0.372	0.801	0.507	0.832	0.607

方法	组件				AUC	F₁
方法	EAM	EFM	MCAM	FFM	AUC	F₁
a.Baseline					0.800	0.595
b.Baseline+EAM	√				0.816	0.617
c.Baseline+EAM+EFM	√	√			0.835	0.634
d.Baseline+EAM+EFM+MCAM	√	√	√		0.841	0.639
e.Baseline+EAM+EFM+MCAM+FFM	√	√	√	√	0.848	0.647

后处理方法	参数值	方法
后处理方法	参数值	本文	MVSS	SPAN	ManTra	GSRNet	DenseFCN	LocateNet	EMTNet
高斯噪声	标准差=3	0.872	0.764	0.164	0.060	0.602	0.802	0.724	0.822
	标准差=9	0.854	0.758	0.165	0.051	0.603	0.749	0.707	0.809
	标准差=15	0.839	0.751	0.165	0.050	0.597	0.690	0.682	0.808
JPEG 压缩	质量因子=50	0.873	0.761	0.250	0.186	0.567	0.811	0.691	0.812
	质量因子=75	0.887	0.770	0.252	0.226	0.569	0.812	0.736	0.825
	质量因子=100	0.891	0.766	0.252	0.457	0.572	0.811	0.738	0.825
高斯模糊	卷积核=3	0.880	0.768	0.249	0.198	0.602	0.802	0.737	0.824
	卷积核=9	0.856	0.742	0.239	0.165	0.583	0.749	0.716	0.811
	卷积核=15	0.815	0.711	0.231	0.161	0.573	0.690	0.645	0.782

Multi⁃scale context⁃aware and boundary⁃guided image manipulation detection method

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