• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Annual Allerton Conference on Communication, Control, and Computing; 20040929-1001; Monticello,IL(US) >The Randlet Transform: Applications to Universal Perceptual Hashing and Image Identification
【24h】

The Randlet Transform: Applications to Universal Perceptual Hashing and Image Identification

机译：Randlet变换：在通用感知哈希和图像识别中的应用

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We introduce a new transform called the randlet transform and explore applications to universal perceptual image hashing (key-based randomized digests) and image identification. Our transform yields robust signal representations that are nearly invariant to several perceptually insignificant transformations (attacks, intentional or otherwise). Our signal representation is hard to guess without the secret key used in its derivation and is motivated by applications such as image identification and watermarking where attack resistance is important. It is also interesting in itself as a transform and in regular signal processing tasks such as scene detection, motion compensation algorithms, compression, etc. We consider the performance of the randlet transform and the wavelet transform against attacks based on rotation, cropping, scaling, additive noise, and JPEG compression. The randlet transform achieves superior performance to the wavelet transform in the task of image identification. For example, a randlet transform with a false positive rate of 2% can detect a 10-degree rotation with a false negative rate of 3.2%, while the best wavelet transform with the same false positive rate has a false negative rate of 38.2%.
机译：我们引入了一种称为randlet变换的新变换，并探索了在通用感知图像哈希（基于密钥的随机摘要）和图像识别中的应用。我们的变换产生的鲁棒信号表示几乎与几个感知上无关紧要的变换（攻击，有意或其他）无关。如果没有在信号推导中使用的秘密密钥，我们的信号表示就很难猜测，并且受到图像识别和水印等应用的推动，在这些应用中，抗攻击性很重要。它本身也很有趣，既可以作为变换，也可以用于诸如场景检测，运动补偿算法，压缩等常规信号处理任务。我们考虑了randlet变换和小波变换针对基于旋转，裁剪，缩放，加性噪声和JPEG压缩。在图像识别任务中，randlet变换实现了优于小波变换的性能。例如，假阳性率为2％的randlet变换可以检测到假阴性率为3.2％的10度旋转，而具有相同假阳性率的最佳小波变换的假阴性率为38.2％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号