计算机应用与软件2023，Vol.40Issue(12)：189-194,6.DOI:10.3969/j.issn.1000-386x.2023.12.028
一种改进的双流Faster R-CNN图像篡改识别模型
IMAGE FORGERY RECOGNITION MODEL BASED ON IMPROVED DUAL-STREAM FASTER R-CNN
摘要
Abstract
At present,most image tampering algorithms can only detect one type of image tampering,and the features of RGB stream and noise stream extracted by the dual-stream Faster R-CNN algorithm are not highly accurate for various image tampering detection.This paper proposes a general improved dual-stream Faster R-CNN image tampering recognition algorithm.By extracting the YCrCb color space of the image,replacing the previous RGB color space,we could find out the traces of tempering better.The three steganalysis rich model(SRM)filters for extracting noise features were rotated to better distinguish the noise inconsistency between the real area and the tampered area,so as to improve the recognition accuracy of the tampered image.Through bilinear pooling,inputting network training and classification,the detection of image tampering and the location of tampering area were completed.In order to verify the performance of the algorithm,experiments were conducted on CASIA and NISIT16 data set.The results show that,compared with the dual-stream Faster R-CNN method,the proposed algorithm improves the average precision(AP)of splicing detection,copy-move detection and removal detection by 0.9,1.5 and 2.6 percentage points respectively.关键词
图像篡改检测/YCrCb流/噪声流/Faster R-CNNKey words
Image forgery detection/YCrCb stream/Noise stream/Faster R-CNN分类
信息技术与安全科学引用本文复制引用
杨衍宇,魏为民,张运琴..一种改进的双流Faster R-CNN图像篡改识别模型[J].计算机应用与软件,2023,40(12):189-194,6.基金项目
上海市自然科学基金项目(16ZR1413100). （16ZR1413100）
