An adaptive LSB matching steganography based on octonary complexity measure

Adaptive steganography methods tend to increase the security against attacks. Most of adaptive methods use LSB flipping (LSB-F) for embedding part of their algorithms. LSB-F is very much vulnerable against simple steganalysis methods but it allows the adaptive algorithms to be extractable at the receiver side. Use of LSB matching (LSB-M) could increase the security but extraction of data at the receiver is difficult or, in occasions, impossible. There are numerous attacks against LSB-M. In this paper we are proposing an adaptive algorithm which, unlike most adaptive methods, uses LSB-M as its embedding method. The proposed method uses a complexity measure based on a local neighborhood analysis for determination of secure locations of an image. Comparable adaptive methods that use LSB-M suffer from possible changes in the complexity of pixels when embedding is performed. The proposed algorithm is such that when a pixel is categorized as complex at the transmitter and is embedded the receiver will identify it as complex too, and data is correctly retrieved. Better performance of the algorithm is shown by obtaining higher PSNR values for the embedded images with respect to comparable adaptive algorithms. The security of the algorithm against numerous attacks is shown to be higher than LSB-M. Also, it is compared with a recent adaptive method and is proved to be advantageous for most embedding rates.     

Anionic clay intercalated by multi-walled carbon nanotubes as an efficient 3D nanofiller for the preparation of high-performance l-alanine amino acid containing poly(amide-imide) nanocomposites

In this study, three-dimensional (3D) nanohybrids with excellent properties were prepared by the simple combination of 1D carbon nanotubes (CNTs) and 2D MgAl-layered double hydroxides (LDHs). An optically active amino acid containing poly(amide-imide) was prepared by direct polycondensation reaction of N-trimellitylimido-l-alanine and 4,4′-diaminodiphenyl methane under green condition in molten tetra-n-butylammonium bromide. PAI/LDH-CNT nanocomposites containing 2, 4, and 8 wt% LDH-CNT were prepared via a simple and an effective ultrasonic method. The presence of CNT in the interlayer space of LDH was confirmed by thermogravimetry analysis, Fourier transformed infrared spectroscopy, and X-ray diffraction techniques. The homogeneous dispersion of nanofillers in the PAI matrix was observed by field emission scanning electron microscopy and transmission electron microscopy. The obtained results revealed the coexistence of exfoliated and intercalated modified LDH-CNT in the polymer matrix.     

Manufacture and characterization of nanocomposite materials obtained from incorporation of d-glucose functionalized MWCNTs into the recycled poly(ethylene terephthalate)

Plastics make considerable amount of solid waste in the worldwide corresponding to their use in many fields of our lives like construction, packaging, building, and so on. The manufacture of fuel-derived plastics is often dangerous to the environment; so, available waste administration systems including energy recovery operations and/or recycling are essential to solve this huge pollution. In this study, poly(ethylene terephthalate) (PET) bottle waste as considerable part of the total plastic waste bulk was recycled and white and fine-recycled PET powders (R-PET) were formed. The obtained R-PET showed semi-crystalline structure according to X-ray diffraction analysis. Then, the effect of functionalized multi-walled CNTs with D-glucose was investigated on R-PET properties. Transmission electron microscopy exhibited good dispersion of MWCNTs-Gl in the R-PET matrix. MWCNT-Gl as a nucleating agent increased the crystallization degree of R-PET.     

Structure and Thermal Degradation Properties of Nanocomposites of Alanine Amino Acid-based Poly(amide–imide) Reinforced with Carboxymethyl-β-cyclodextrin Intercalated in a Layered Double Hydroxide

MgAl-layered double hydroxide was intercalated with carboxymethyl-β-cyclodextrin under ultrasonic conditions. Then, different percentages of carboxymethyl-β-cyclodextrin–functionalized layered double hydroxide (carboxymethyl-β-cyclodextrin-layered double hydroxide) was incorporated into the alanine amino acid-based poly(amide–imide) matrix via ultrasonic irradiations to obtain poly(amide–imide)/carboxymethyl-β-cyclodextrin-layered double hydroxide nanocomposites. The surface morphology and thermal degradation behavior of the poly(amide–imide)/carboxymethyl-β-cyclodextrin-layered double hydroxide nanocomposites have been examined by different techniques. The relatively good dispersion of carboxymethyl-β-cyclodextrin-layered double hydroxide into the poly(amide–imide) matrix was detected by transmission electron microscopy. Transmission electron microscopy images represented that the layered double hydroxide platelets were oriented separately and were not detected by X-ray diffraction, which means, the layered double hydroxide layers are exfoliated in the polymer.     

Steganalysis and payload estimation of embedding in pixel differences using neural networks

In this paper a steganalysis technique is proposed for pixel-value differencing method. This steganographic method, which is immune against conventional attacks, performs the embedding in the difference of the values of pixel pairs. Therefore, the histogram of the differences of an embedded image is different as compared with a cover image. A number of characteristics are identified in the difference histogram that show meaningful alterations when an image is embedded. Five distinct multilayer perceptrons neural networks are trained to detect different levels of embedding. Every image is fed to all networks and a voting system categorizes the image as stego or cover. The implementation results indicate 88.6% success in correct categorization of the test images that contained more than 20% embedding. Furthermore, using a neural network an estimator is presented which gives an estimate of the amount of the MPVD embedding in an image. Implementation of the estimator showed an average accuracy of 88.3% in the estimation of the amount of embedding.