HRDA-Net：面向真实场景的图像多篡改检测与定位算法

朱叶; 余宜林; 郭迎春

doi:10.11959/j.issn.1000-436x.2022016

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HRDA-Net：面向真实场景的图像多篡改检测与定位算法

学术通信 | 更新时间：2024-06-05

- HRDA-Net：面向真实场景的图像多篡改检测与定位算法
- HRDA-Net: image multiple manipulation detection and location algorithm in real scene
- 通信学报 2022年43卷第1期页码：217-226
- 作者机构：
  
  1. 河北工业大学人工智能与数据科学学院，天津 300401
  2. 深圳市媒体信息内容安全重点实验室，广东深圳 518060
- 作者简介：
  
  [ "朱叶（1989- ），女，山东菏泽人，博士，河北工业大学讲师、硕士生导师，主要研究方向为图像安全取证、图像处理与模式识别" ]
  [ "余宜林（1998- ），男，福建南平人，河北工业大学硕士生，主要研究方向为图像安全取证" ]
  [ "郭迎春（1970- ），女，河北张家口人，博士，河北工业大学副教授、硕士生导师，主要研究方向为图像处理与模式识别、人工智能等" ]
- 基金信息：
  
  国家自然科学基金资助项目(62102129);国家自然科学基金资助项目(61806071);国家自然科学基金资助项目(91746207);河北省自然科学基金资助项目(F2021202030);河北省自然科学基金资助项目(F2020202025);河北省自然科学基金资助项目(F2019202381);河北省自然科学基金资助项目(F2019202464);河北省高等学校科学技术研究基金资助项目(QN2019207);河北省高等学校科学技术研究基金资助项目(QN2020185)
- DOI：10.11959/j.issn.1000-436x.2022016
  中图分类号： TP391
- 网络出版日期：2022-01，
  
  纸质出版日期：2022-01-25
- 稿件说明：
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朱叶, 余宜林, 郭迎春. HRDA-Net：面向真实场景的图像多篡改检测与定位算法[J]. 通信学报, 2022,43(1):217-226.

Ye ZHU, Yilin YU, Yingchun GUO. HRDA-Net: image multiple manipulation detection and location algorithm in real scene[J]. Journal on communications, 2022, 43(1): 217-226.
朱叶, 余宜林, 郭迎春. HRDA-Net：面向真实场景的图像多篡改检测与定位算法[J]. 通信学报, 2022,43(1):217-226. DOI： 10.11959/j.issn.1000-436x.2022016.

Ye ZHU, Yilin YU, Yingchun GUO. HRDA-Net: image multiple manipulation detection and location algorithm in real scene[J]. Journal on communications, 2022, 43(1): 217-226. DOI： 10.11959/j.issn.1000-436x.2022016.

摘要

针对主流篡改数据集单幅图像仅包含一类篡改操作，且对真实图像定位存在“伪影”问题，构建面向真实场景的多篡改数据集（MM Dataset），每幅篡改图像包含拼接和移除2种篡改操作。针对多篡改检测与定位任务，提出端到端的高分辨率扩张卷积注意力网络（HRDA-Net），利用自顶向下扩张卷积注意力（TDDCA）模块融合图像 RGB 域和 SRM 域特征。最后，采用混合扩张卷积模块（MDC）分别提取拼接、移除和篡改检测任务特征，实现篡改区域定位和篡改置信度预测。为提高网络训练效率，提出余弦相似度损失函数作为辅助损失。实验结果表明，在MM Dataset下，与主流语义分割方法相比，HRDA-Net具有较优的性能和较强的稳健性；在单篡改数据集CASIA和NIST下，与主流单篡改定位方法相比，HRDA-Net的F1和AUC分数均较优。

Abstract

Aiming at the problems that the fake image just contains one tampered operation in mainstream manipulation datasets and the artifact is a common problem in manipulation location.The multiple manipulation dataset (MM Dataset) was constructed for real scene

which contained both splicing and removal in each images.Based on this

an end-to-end high-resolution representation dilation attention network (HRDA-Net) was proposed for multiple manipulation detection and localization

which fused the RGB and SRM features through the top-down dilation convolutional attention (TDDCA).Finally

the mixed dilated convolution (MDC) would respectively extract the features of splicing and removal

which could realize multiple manipulation location and confidence prediction.The cosine similarity loss was proposed as auxiliary loss to improve the efficiency of network.Experimental results on MM Dataset indicate that the performance and robustness of HRDA-Net is better than semantic segmentation methods.Furthermore

the scores of F1 and AUC are greater than state-of-the-art manipulation location methods in CASIA and NIST datasets.

关键词

Keywords

references

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