TY -的A2 -古普塔,Suneet Kumar盟——Kadam Kalyani Dhananjay AU - Ahirrao,斯瓦特盟——Kotecha Ketan PY - 2022 DA - 2022/01/05 TI -有效的方法复制移动探测和识别以及图像拼接伪造使用面具R-CNN MobileNet V1 SP - 6845326六世- 2022 AB -技术进步的现代,容易获得的图像编辑工具,大大减少了成本,费用,和专业知识需要利用视觉篡改和延续有说服力。借助著名的在线平台如Facebook, Twitter, Instagram,操纵图像分布全球。在线平台的用户可能不知道的存在和传播伪造图像。这些图片对社会产生重大影响,可能会误导决策过程在卫生保健领域,体育、犯罪调查,等等。此外,改变图像可以用来宣传误导信息干扰民主进程(例如,选举和政府立法)和危机的情况下(例如,流行病和自然灾害)。因此,迫切需要有效的方法探测和识别伪造的。目前各种技术用于识别和检测这些伪造的。传统技术依赖于手工或shallow-learning特性。在传统的技术,从图片可以是一个具有挑战性的任务,选择特性,研究者必须决定哪些特性是重要的,哪些不是。同样,如果特性提取的数量相当大,特征提取使用这些技术可以成为耗时且乏味。 Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image. SN - 1687-5265 UR - https://doi.org/10.1155/2022/6845326 DO - 10.1155/2022/6845326 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -