A high payload steganographic algorithm based on edge detection

ABSTRACT:

The least-significant-bit (LSB) technique is one of the commonly used steganographic algorithms in the spatial domain. In most existing schemes, they didn’t carefully analyze the relationship between the image content itself. Hence, the smooth areas in the cover image will inevitably be contaminated after hiding even at a low embedding rate, thereby leading to poor visual quality and low security. In recent years, diverse steganography methods using edge detection have been proposed. However, their schemes employ certain pixels in the cover image for the sake of storing edge information, resulting in significant embedding distortion and low payload. In this study, a novel steganography approach based on the combination of LSB substitution mechanism and edge detection is proposed. To avoid the excavation of human visual system (HVS) when more secret bits are embedded into pixels, we classify the cover pixels into edge areas and non-edge areas. Then, pixels that belong to the edge area are used to carry more secret bits. In addition, to further increase the payload as well as preserve good image quality, we adopt a skillful way that the edge information is determined by most significant bits (MSBs) of the cover image so that it does not need to be stored. In the extraction phase, the same edge information is obtained. Therefore, the secret data can be correctly extracted without confusion. The experimental results demonstrate that our scheme achieves a much higher payload and better visual quality than those of state-of the-art schemes.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

Jana et al. proposed new reversible data hiding scheme based on high relation of two consecutive pixels for embedding n_1 secret bits. To enhance the security of embedded data, the shared secret key bit stream is used to distribute the stego pixel pair into dual images. However, the embedding capacity and image quality of their scheme are limited, i.e. 0.5 bpp and smaller than 45 dB, respectively. In principle, the edge areas are usually less sensitive to changes than the smooth areas.
Chen et al. presented a high payload steganography method by using the hybrid edge detector in 2010. Since different numbers of secret bits are concealed into each pixel, their scheme can resist to statistical analysis, meaning a relatively high level of security.
Tseng and Leng extended the scheme by the combination of Canny edge detector (CED) and fuzzy logic edge detector (FLED) instead of using fuzzy complement edge detector.

DISADVANTAGES OF EXISTING SYSTEM:

The computational complexity is considerably high since it takes every possibility into account. It is clear that the existing methods used some pixels to carry indices that represent the edge information. As a result, this process causes embedding space to be lost significantly.
It may bring in a shortcoming that the amount of secret data which can be embedded usually varies widely from region to region in a digital image. Not all the pixels can bear the same degree of changes.

PROPOSED SYSTEM:

To solve aforementioned weaknesses of the existing schemes, in this paper, we propose a more skillful scheme based on edge detection.
Our approach is based on the LSB substitution approach combined with edge detector which employs the principle that edge areas can tolerate the number of embedding bits more than smooth areas according to HVS. In the proposed scheme, three edge detectors Canny, Sobel, and Fuzzy are utilized to demonstrate that our embedding rule can always work well under different environments.

ADVANTAGES OF PROPOSED SYSTEM:

In the proposed scheme edge information is handled simultaneously during embedding procedure, none of extra information is required for storing in the stego image; it means more embedding space is preserved for embedding secret data and the image quality is improved further.
It contributes to creating larger embedding space than the existing schemes since the pixels in the image segment abandon acting as indices to store edge information.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium Dual Core.

Hard Disk : 120 GB.

Monitor : 15’’ LED

Input Devices : Keyboard, Mouse

Ram : 1GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 7.
Coding Language : MATLAB.
Tool : MATLAB R2013A

REFERENCE:

Junlan Bai a, Chin-Chen Chang b, Thai-Son Nguyen c, Ce Zhu a, Yanjun Li, “A high payload steganographic algorithm based on edge detection”, Displays 46 (2017) .

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A high payload steganographic algorithm based on edge detection