基于非局部自相关的复制粘贴检测算法

doi:10.16180/j.cnki.issn1007-7820.2022.10.010

Abstract

Abstract:

On account of the problem that forgery target and source of digital image copy-move manipulation cannot be distinguished, this study improves the similarity matching algorithm and uses non-local self-attention mechanism to solve the classification problem of copy-move forgery source and target areas, under the premise that manipulated regions are detected. The overall framework is a dual-branch detection network. The main branch uses the classic U-net to segment the pixel of forgery regions, and the auxiliary branch uses the siamese network to extract features and calculate the autocorrelation to separate the forgery targets and source area pixels. Finally, three-categories results can be predicted by end-to-end training after fusing two branches. The experiment result shows that the pixel-level classification accuracy of the proposed algorithm when detecting the localized target area reaches 80.47%, and the F1 value and accuracy are better than the compared algorithm. The visualization results and robustness experiments also show that the proposed algorithm has excellent generalization performance.

Key words: siamese network, image passive forensic, copy-move forgery, similarity distance, non-local operator, self-correlation, encoder-decoder, end-to-end

CLC Number:

TP391.41

WU Xu,LIU Xiang. Copy-Move Forgery Detection Algorithm Based on Non-Local Self-Correlation[J].Electronic Science and Technology, 2022, 35(10): 59-64.

Figures/Tables 9

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References 21

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评估指标	区域	文献[14]	文献[15]	本文算法
Precision	原始	90.23%	90.91%	91.78%
	来源	25.46%	30.37%	35.58%
	目标	13.27%	33.93%	34.81%
Recall	原始	97.48%	97.22%	97.38%
	来源	14.41%	22.59%	25.83%
	目标	5.89%	23.77%	24.94%
F1-Score	原始	93.71%	93.69%	94.50%
	来源	18.40%	25.91%	29.93%
	目标	8.16%	27.96%	29.06%

评估指标	区域	BusterNet	cGANs	本文算法
Precision	原始	93.96%	95.53%	97.66%
	来源	66.93%	70.42%	78.92%
	目标	48.67%	72.26%	80.47%
Recall	原始	97.14%	95.48%	96.40%
	来源	50.40%	58.84%	63.18%
	目标	21.74%	49.76%	60.79%
F1-Score	原始	95.52%	95.50%	97.03%
	来源	57.50%	64.11%	70.18%
	目标	30.05%	58.94%	69.26%

攻击手段		BusterNet	cGANs	本文算法
JPEG压缩	60%	0.47	0.70	0.83
	70%	0.41	0.66	0.79
	80%	0.35	0.61	0.70
亮度	线性变换	0.49	0.73	0.81
	对数变换	0.46	0.71	0.80
	指数变换	0.45	0.71	0.79
模糊	高斯	0.43	0.72	0.82
	均值	0.40	0.74	0.82
	中值	0.42	0.75	0.80
尺度变换	缩小0.5倍	0.46	0.73	0.80
	放大2倍	0.47	0.75	0.81
	放大1倍	0.47	0.75	0.83
噪声	高斯	0.37	0.65	0.74
	平滑	0.36	0.63	0.75
	伽马	0.34	0.66	0.71

Copy-Move Forgery Detection Algorithm Based on Non-Local Self-Correlation

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