LWT based encrypted payload steganography

Steganography is used in covert communication for transportation of secrete information. In this paper we propose LWT based Encrypted Payload Steganography (LEPS). The payload is segmented into two parts say block 1 and block 2. The LWT is applied on block 2 to generate four sub bands. Payload block 1 is retained in the spatial domain itself. The values of approximation band coefficients of block 2 and spatial domain intensity values of block 1 are compressed. The LWT is applied on cover image to generate wavelet sub bands and considered only diagonal sub bands (XD). The XD band is decomposed into three parts. The key values are embedded into first part of XD band. The compressed payload is embedded in second and third blocks of XD adaptively. The payload can be retrieved at the destination by adapting reverse process of embedding. It is observed that the values of PSNR and capacity are better in the case of proposed algorithm compared to existing algorithm

Conditional Entrench Spatial Domain Steganography

Steganography is a technique of concealing the secret information in a digital carrier media, so that only the authorized recipient can detect the presence of secret information. In this paper, we propose a spatial domain steganography method for embedding secret information on conditional basis using 1-Bit of Most Significant Bit (MSB). The cover image is decomposed into blocks of 8*8 matrix size. The first block of cover image is embedded with 8 bits of upper bound and lower bound values required for retrieving payload at the destination. The mean of median values and difference between consecutive pixels of each 8*8 block of cover image is determined to embed payload in 3 bits of Least Significant Bit (LSB) and 1 bit of MSB based on prefixed conditions. It is observed that the capacity and security is improved compared to the existing methods with reasonable PSNR

Embedding information in DCT coefficients based on average covariance

Embedding information in DCT coefficients based on average covarianc

Image steganography using non embedding and average technique in transform domain

The steganography is an art and science of hiding information into a given media to ensurethe security of information over the communication channel. In this paper we propose a Image Steganography using Average Technique in Transform Domain (ISATT). Th

Secure steganography using hybrid domain technique

The steganography is used to hide information and communicate in secure way to the destination. In this paper we propose Secure Steganography using Hybrid Domain Technique (SSHDT). The cover image of different formats and sizes are considered and resized to dimensions of power of 2. The Daubechies Lifting Wavelet Transforms (LWT) is applied on cover image to generate four sub bands XA, XH, XV and XD. The XD band is considered and divided into two equal blocks say upper and lower for payload embedding. The payload of different formats are considered and resized to dimensions of power of 2. The payload is fragmented into four equal blocks. The integer Haar LWT is applied on two diagonal sub bands to generate F1 and F2 blocks and remaining two diagonal sub bands are retained in the spatial domain itself say S1 and S2. The S1, S2. F1 and F2 matrix are converted into single columns. The bit reversal is applied on each element of a column to scramble the payload index bit positions. The cube root is applied on coefficient of index value to scale down the magnitudes. The integer part is considered for embedding in XD band only of cover image to generate stego object. The Decision Factor Based Manipulation (DFBM) is used to scramble further stego object to improve security to the payload. Dubechies Inverse LWT (ILWT) is applied on XA, XH, XV and XD stego object to obtain stego image in spatial domain. it is observed that PSNR and embedding capacity of the proposed algorithm is better compared to the existing algorithm. © 2012 IEEE

Covariance based steganography using DCT

The confidential information is communicated through the open channel in a covert way by using steganography. In this paper we propose the Covariance based Steganography using Discrete Cosine Transform (CSDCT) algorithm. The Average Covariance of the Cover Image (ACCI) is computed and threshold ACCI value is fixed at 0.15. The cover image is segmented into 8*8 cells and the Least Significant Bit (LSBs) are replaced by Most Significant Bits (MSBs) of payload based on ACCI values. It is observed that the capacity, Peak Signal to Noise Ratio (PSNR) and security is better compared to the existing algorithm. © 2011 Springer-Verlag

Mantissa replacement steganography using LWT

Steganography is an authenticated technique for maintaining secrecy of embedded data. The novel concept of replacing mantissa part of cover image by the generated mantissa part of payload is proposed for higher capacity and security. The Lifting wavelet Transform (LWT) is applied on both cover image and payload of sizes a * a and 3a * 2a respectively. The mantissa values of Vertical band (CV), Horizontal band (CH) and diagonal band (CD) of cover image are removed to convert into real values. The approximation band of payload is considered and the odd column element values and even column element values are divided by 300 and 30000 respectively to generate only mantissa part of payload. The modified odd and even column vector pairs are added element by element to form one resultant vector. The column vector elements of cover image and resultant column vector elements of payload are added

DTCWT based high capacity steganography using coefficient replacement and adaptive scaling

The steganography is used for secure communication. In this paper we propose Dual Tree Complex Wavelet Transform (DTCWT) based high capacity steganography using coefficient replacement and adaptive scaling. The DTCWT is applied on cover image and Lifting Wavelet Transform2 (LWT2) is applied on payload to convert spatial domain into transform domain. The new concept of replacing HH sub band coefficients of DTCWT of cover image by LL sub band coefficients of payload is introduced to generate intermediate stego object. The adaptive scaling factor is used based on entropy of cover image to scale down intermediate stego object coefficient values to generate final stego object. It is observed that the capacity and security are increased in the proposed algorithm compared to existing algorithms

Human ear identification system using shape and structural features based on SIFT and ANN classifier

People have become more interested in biometrics recognition as technology has advanced. Biometrics is the study of automatic systems for distinguishing people related to physical or behavioural features. Methods for finding favourable biometric traits have acquired more attention in recent years. The ear is among the most consistent biometric traits because it does not alter with aging or emotion. The human ear is an excellent source of data for passive personal authentication. The ear looks to be an excellent potential solution because it is accessible, images are easily obtained, and the ear structure doesn't change over the years. The ear meets a biometric criterion (universality, distinctiveness, durability, and collectability). The biometric field is working hard to develop automated individual identification using ear images. Several face recognition approaches fail in this present COVID-19 outbreak due to the mask-wearing situation. The human ear is attractive data for passive personal authentication because it does not need the individual's involvement to recognize the ear. In this research, we suggested an automatic ear identification system using Scale Invariant Feature Transform (SIFT) and an Artificial Neural Network (ANN)

Human Ear Identification System Using Shape and Structural Features Based on SIFT and ANN Classifier

People have become more interested in biometrics recognition as technology has advanced. Biometrics is the study of automatic systems for distinguishing people related to physical or behavioural features. Methods for finding favourable biometric traits have acquired more attention in recent years. The ear is among the most consistent biometric traits because it does not alter with aging or emotion. The human ear is an excellent source of data for passive personal authentication. The ear looks to be an excellent potential solution because it is accessible, images are easily obtained, and the ear structure doesn't change over the years. The ear meets a biometric criterion (universality, distinctiveness, durability, and collectability). The biometric field is working hard to develop automated individual identification using ear images. Several face recognition approaches fail in this present COVID-19 outbreak due to the mask-wearing situation. The human ear is attractive data for passive personal authentication because it does not need the individual's involvement to recognize the ear. In this research, we suggested an automatic ear identification system using Scale Invariant Feature Transform (SIFT) and an Artificial Neural Network (ANN)