...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of visual communication & image representation >Steganalytic feature based adversarial embedding for adaptive JPEG steganography
【24h】

Steganalytic feature based adversarial embedding for adaptive JPEG steganography

机译：基于STEGANALYTIC的对抗嵌入自适应JPEG隐写术

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we present a novel adversarial embedding scheme named Steganalytic Feature based Adversarial Embedding (SFAE), which is elaborately designed in a non-data-driven style. Firstly, a novel DCTR based adversary is designed to generate adversarial stego images which can not only resist feature based steganalysis but also deep learning based steganalysis. Specifically, our adversary consists of an end-to-end neural network structure, while its inner weights are set according to DCTR rather than learned from datasets. Secondly, we use the minimum distance to the cover in steganalytic space as the criterion to select the optimal adversarial stego image, rather than fooling the adversary. Last but not least, we present two SFAE implementations to adapt to different cases. One is Iterative SFAE, which needs to calculate gradients multiple times. Iterative SFAE is more secure but has higher complexity. It fits the case that the steganographer has adequate computing resources. Another implementation is Oneshot SFAE, which can calculate gradients once. Oneshot SFAE trades the security for lower complexity. It fits the steganographer that has stricter requirements for running time. Experiments demonstrate that SFAE is effective to improve the security of conventional steganographic schemes against the state-of-the-art steganalysis including both feature based steganalysis and deep learning based steganalysis.
机译：在本文中，我们提出了一种名为STEGANALYTIC FATHER基于对抗性嵌入（SFAE）的新型对抗性嵌入方案，其在非数据驱动的风格中精心设计。首先，基于DCTR基于的对手设计用于产生不仅可以抵抗基于特征的麻木分析而且基于深度学习的隐藏的侵权的STEGO图像。具体而言，我们的对手由端到端的神经网络结构组成，而其内部权重根据DCTR而不是从数据集中学到的。其次，我们使用落地空间中的封面的最小距离作为选择最佳对抗的STEGO图像的标准，而不是欺骗对手。最后但并非最不重要的是，我们提出了两个SFAE实现，以适应不同的情况。一个是迭代SFAE，需要多次计算梯度。迭代SFAE更安全但具有更高的复杂性。它适合托克光师拥有足够的计算资源的情况。另一个实现是Oneshot SFAE，可以计算渐变一次。 OneShot SFAE交易安全性较低的复杂性。它适合带来严格的运行时间要求的斯托克光师。实验表明，SFAE可以有效地改善常规隐杂化方案的安全性，而最先进的隐草，包括基于特征的隐星分析和基于深入的学习的隐星分析。

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