An efficient steganographic framework based on dynamic blocking and genetic algorithm

Abstract

An important property of any robust steganographic method is that it must introduce minimal distortion in the created stego-images. This objective is achieved if one can maximize the similarity between the pixels value of the cover image and the secret data. In the proposed framework, the maximal similarity is obtained by arranging some routes along the pixel positions. Our novel method is based on dynamic blocking and the genetic algorithm which decreases the distortion produced by a base data embedding method. In the proposed parametric framework, the cover image is first divided into several horizontal static-size strips. Then each strip is partitioned into some dynamic-size blocks. The size of each block is determined using the genetic algorithm such that minimal distortion is produced. Traversing the blocks of a strip in a raster scan manner, the route for embedding the data into the strip is obtained. The best route is considered to be the one which partition a strip into different blocks with different sizes. The embedding route is raster scan of the partitioned blocks. In our framework, only the sizes of the blocks need to be recorded as the overhead instead of the routes. The experimental results evaluated on 2000 natural images using several steganalytic algorithms demonstrate that our proposed method decreases the image distortion and thus enhances the security.

A New Steganographic Algorithm Based on Multi Directional PVD and Modified LSB

“…Image steganographic algorithms, which have been discussed in literature, can be classified based on the embedding domain into two main classes: spatial domain and transform domain [14,[25][26][27]. The spatial domain algorithms are most frequently used because of their good concealment, great capability to hide information, and ease of realization [28].…”

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors

“…The main steganographic methods in the spatial domain [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ] are LSB-based (Low Significant Bit). Recently, entropy has also been extensively used to support data-hiding algorithms [ 18 , 19 , 20 ].…”

Extreme learning machine based optimal embedding location finder for image steganography

“…This shortcoming can be overcome by introducing the neural network (NN) based steganographic technique, where the NN uses a distributed representation to store the learning knowledge. Thus, accessing the concealed data without knowing the topology of the NN appears practically infeasible [ 1 ]. Although some researchers prefer models with interpretability power such as explicit mathematical or statistical models or even heuristically encoded models such as fuzzy models, it has been proved that black box type of models when learning is feasible have more capability of capturing complicated knowledge and proving functionality in real world type of systems [ 2 ][ 3 ][ 4 ].…”

“…Furthermore, spatial or frequency domain techniques are integrated with other techniques including artificial NN (ANN), genetic algorithm (GA), or both to attain enhanced steganographic performances. Spatial-domain based GAs are used [ 1 ], [ 24 ] to minimize the distortion and. GA and ANN are used [ 25 ] to accelerate the training speed.…”

“…GA is used to model the steganography problem [ 24 ] for search and optimization. Besides, for optimization with minimum distortion the GAs are utilized, where a stego image closer to the cover image is obtained [ 1 ], [ 35 ]. The [ 11 ] presents DCT with Markov as a detection and classifier for images.…”