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首页> 外文期刊>International Journal of Engineering Research and Applications >Efficient Steganography Method to Implement Selected Lease Significant Bits in Spatial Domain (SLSB – SD)
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Efficient Steganography Method to Implement Selected Lease Significant Bits in Spatial Domain (SLSB – SD)

机译:一种有效的隐写方法,可在空间域中实现选定的租赁有效位(SLSB – SD)

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In order to improve the capacity of the hidden secret data and to provide an imperceptible stego-image quality, a novel steganographic method based on least-significant-bit (LSB) replacement method is presented. First, a different value from two consecutive pixels by utilising the PVD method the security level is the same as previous. In the LSB matching, the choice of whether to add or subtract one from the cover image pixel is random. The new method uses the choice to set a binary function of two cover pixels to the desired value. The embedding is performed using a pair of pixels as a unit, where the LSB of the first pixel carries one bit of information, and a function of the two pixel values carries another bit of information. we expand the LSB matching revisited image stegano graphy and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameter the proposed method show better performance than traditional LSB matching in terms of distortion and resistance against existing steganalysis.
机译:为了提高隐藏秘密数据的容量并提供隐秘的隐秘图像质量,提出了一种基于最小有效位(LSB)替换方法的隐秘方法。首先,通过使用PVD方法,两个连续像素的值不同,安全级别与先前相同。在LSB匹配中,是选择从封面图像像素中添加还是减去一个都是随机的。新方法使用该选项将两个覆盖像素的二进制函数设置为所需值。以一对像素为单位执行嵌入,其中第一像素的LSB承载一位信息,而两个像素值的函数则承载另一位信息。我们扩展了LSB匹配重新访问图像隐写图形,并提出了一种边缘自适应方案,该方案可以根据秘密消息的大小和封面图像中两个连续像素之间的差异来选择嵌入区域。对于较低的嵌入率,仅使用较尖锐的边缘区域,同时保持其他较平滑的区域不变。当嵌入率增加时,只需调整几个参数,就可以自适应地释放更多边缘区域以隐藏数据。在失真和对现有隐写分析的抵抗力方面,所提出的方法表现出比传统LSB匹配更好的性能。

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