StegoCrypt method using wavelet transform and one-time pad for secret image delivery

A reversible extended secret image sharing scheme based on Chinese remainder theorem

A user-friendly secret image sharing scheme with reversible steganography based on cellular automata

Speech signal carries much more information than spoken words and speaker’s information. Noise present in the speech signal is an obstacle in the path of analysing the speech signal precisely. Therefore, speech signal denoising has an important role in the speech signal analysis. In this paper, Wavelets are explored for pre-processing and suppression of noise from the noisy speech signals. The main aim of the proposed methodology involves, the pre-processing of the speech signals by using the combination of Discrete Wavelet Transform (DWT) and hard thresholding and to evaluate the result to find the optimal wavelet family with there optimal order followed by the suitable level of decomposition. This paper involves the exploration of the Wavelet Family and an improved Signal to Noise Ratio (SNR) values of the acquired speech signal which is shown in the result analysis. KeywordsSpeech signalsDiscrete wavelet transform (DWT)Wavelet transform (WT)Hard thresholdingSignal to noise ratio (SNR)

Steganography is an art of activity the data within the cover like the simplest way that, it's like straightforward cover, though its hidden information. In this paper, secret image is activity into two completely different domains like as IWT (Integer Wavelet Transform) and DWT (Discrete Wavelet Transform). The cover image and secret image coefficient values square measure embedded by 512*512 exploitation fusion process techniques. We tend to applied numerous combinations of DWT and IWT on each images and obtained a decent quality stego images. The each domain offers safer with secret key and sure robustness of our algorithm. This dual wavelet transform utilized in Color Image Steganography Method (DWTSM) in Blue Channel (B-Channel) provides high capability and security. This proposed algorithm is tested with image quality parameters and compared to other algorithms. The experimental Results show that dual based approach achieved high capability and high security of our system in B-Channel and conjointly improves the performance of steganography system. The proposed algorithm achieved high PSNR quantitative relation 45 to 55 and other parameter values achieved the best solution and this methodology compared to different combination of transform.


 
 
 A hybrid steganographic technique has been proposed to enlarge the size of secret message and to hide several images in one cover image. Initially, the secret images were compressed using JPEG 2000 before embedding them to the cover image. Second least significant bit plane of cover image was entirely replaced by secret message while tri-ways-pixel-value-differencing (TPVD) was used to hide secret message in remaining seven-bit cover image. The quality of stego image was enhanced by residual image coding (RIC). The cover image was divided in four sub-images, three of them provided cover for secret message using proposed technique while fourth sub-image was used to embed RIC of stego image using only TPVD, to ensure better quality in terms of peak signal to noise ratio (PSNR). Proposed technique provided the opportunity to transmit secret data without detection by different steganalysis techniques like dual statistics steganalysis. Experimental results verified that proposed technique achieved high payload with better image quality than TPVD scheme.
 
 

Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and keyin color cover image using Discrete Wavelet Transform (DWT).There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.

Face Recognition is a prototype with stirring functions in the direction of facial features for consent and validation. In this approach, a particular type of face recognition systems is bond different feature extraction approaches to conquer the image entity configuration. To solve face recognition under unreliable pose is rigorous pose invariant features with effective approach. In this work, an algorithm is implemented in MATLAB and performance evaluation of achievement rate with existing techniques is proposed. Also, the performance of the face recognition system is based on feature extractions using wavelet transform and demonstrates the improved quality image. This work provides Artificial Neural Network (ANN) for face recognition and using a combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) algorithm for feature extraction and acquires a transformed face image to raise the features. The results demonstrated that the proposed system has higher accuracy, precision and sensitivity than face recognition using different feature extractors.

Steganography is a method for hiding information in a host signal. The host signal can be a still image, speech or video and the message signal that is hidden in the host signal can be a text, image or an audio signal. In this method host signal is image and secret message is audio signal. In this paper, a data hiding scheme by Discrete Wavelet Transform with LSB substitution is proposed. By applying Discrete Wavelet Transform on secret message, message data is hidden in the LSB's of the colour image pixels. The worst case mean-square-error and PSNR between the stego-image and the cover-image is derived. Experimental results show that the stego-image is visually indistinguishable from the original cover-image. In this paper male/female speeches are hiding inside digital images. Discrete Wavelet Transform (DWT) is applied on secret speech signal followed by LSB method to obtain the stego-image at transmitter side. At receiver end Inverse Discrete Wavelet transform applied on stego image and secret signal can retrieve by LSB method.

Internet of Medical Things (IoMT) systems generate medical data transmissions between patients, medical experts, and medical centers over public networks, which require high levels of security to protect the content of medical images and the personal information they contain. In this paper, we propose a new stego image encryption scheme based on a new secret image compression method, wavelet transformation, QR decomposition of the cover image, and a new chaotic map. The secret image is compressed by the Hahn-Krawtchouk hybrid quaternion square moments (HK-HQSM), which are optimized by a new hybrid metaheuristic algorithm based on the Salp Swarm Algorithm (SSA) and the Arithmetic Optimization Algorithm (AOA). To increase the security level when transmitting the proposed stego images over public networks, we introduce a new chaotic map based on the 2D fractional Henon map to encrypt the stego image. To demonstrate the effectiveness of the proposed steganography scheme for IoMT, we implemented this scheme on a low-cost Raspberry Pi 4 hardware board. The results of the performed numerical experiments show that our method is secure and provides exceptional robustness against common standard image processing attacks (steganalysis attacks). The results also demonstrate that our strategy is able to work efficiently and quickly when implemented on a Raspberry Pi board.

This paper presents a new secret sharing scheme for identification of cheaters in the elections. In a proposed scheme secret image (voter ID) is embedded into cover image (Indian emblem) of same size and generates n stego images. The resulting stego images are visually good and look like a cover image. Various image quality metrics are used to assess the quality of stego images. Out of n stego images only last two stego images are required to reconstruct the secret and cover images without any loss. The proposed scheme offers high embedding capacity. Experimental results of proposed scheme shown to be superior in terms of PSNR and visual quality than existing schemes.

One of the container media that is available and popular is the Joint Photographic Experts Group (JPEG) format image. This article aims to determine the effect of Quality Factor on the secret message capacity of JPEG image steganography and stego image quality. The quality of an image can actually be seen subjectively with the human eye, but this is relative between each individual. Because the assessment of the human eye varies from person to person. In addition, the effect of Quality Factor on secret message capacity is not yet known whether it has an impact. Therefore, in this study the Quality Factor is used to objectively see the secret message capacity of the JPEG image steganography and the quality of the stego image. The parameter used to determine the quality of an image is the Peak Signal to Noise Ratio (PSNR). PSNR will compare the quality of the original image (before steganography) with the stego image. The test results show that the Q Factor effect can affect the secret message capacity of the JPEG image steganography and the stego image quality. The bigger the Q Factor, the more the message capacity is generated. The greater the Q factor, the better the quality of the resulting stego image.

Image security on web exchanges is the major concern of the hour as the breaching attacks into the image databases are rising every year. Numerous researchers are investigated the image security with Steganography, cryptography, encryption, watermarking et cetera, our proposed model image Steganography with Secret Share cryptography (SSC) is considered to upgrade the security level, here Medical images are considered for stego image creation process. In the wake of inserting of secret data with cover image Optimal Discrete Wavelet Transform (DWT) used to transform the area, here Daubechies (db2) coefficients are utilized, in addition upgrading the PSNR Continues Harmony Search (CHS) used to enhance those coefficients. At last, SS are made for lower band stego images with high security process. Visual Cryptography is utilized to encrypt a secret image into redid adaptations of the first image which prompts computational unpredictability and furthermore create share. In view of above process the secret data or image hided and anchored, finally apply converse procedure to recover the first image. From the execution results, PSNR, hiding capacity, error rate is computed, its contrasted with existing Techniques.

Histogram-shifting-imitated reversible data hiding

Feature extraction plays a very important role in the speech classification process because a better feature is good for improving the classification rate. This paper presents a speech feature extraction method by using Discrete Wavelet Transform (DWT) at 7th level of decomposition with mother wavelet of Dau-bechies 2, Renyi Entropy (RE), Autoregressive Power Spectral Density (AR-PSD), Statistical, as well as the combination of each method for extracting and classifying the certain Indonesian velar-vowel and alveolar-vowel syllables. Five different features set used in this study, namely the combination features of DWT and statistical (WS), RE, the combination of AR-PSD and Statistical (PSDS), the combination of PSDS and the selected features of RE (RPSDS), and the combination of DWT, RE, and AR-PSD (WRPSDS). Each syllable is segmented at a certain length to form a consonant-vowel. Multi-layer perceptron is used as a classifier after feature extraction process. The results show that the rank of the average recognition rate are WRPSDS, WS, RPSDS, PSDS, and RE, respectively.

This paper presents a novel feature extraction framework for content-based image retrieval (CBIR). Discrete wavelet transform (DWT) based Local tetra pattern (LTrP) is used to obtain the feature map from an input image. Decomposition of DWT up to single level and the features obtained from it would make the CBIR system very sensitive to noise. Therefore, decomposition up to three scales is used to remove noise. On each of the sub band LTrP is applied and 130 features are extracted. Further, Artificial Neural Network (ANN) is employed for index matching and image retrieval task which gives the classification accuracy of 97.9 % for Corel 1K database. We have compared our proposed feature extraction scheme with LTrP and other existing local descriptor. Results show that combination of DWT and Local Tetra Patterns (DWT + LTrP) extracts more robust features than LTrP alone. Also, the effect of different wavelet filters on the accuracy of the system has been analyzed. Proposed framework has been tested on Corel-1000 and Corel-10000 databases. We have used average retrieval rate, precision, and recall for performance measure of the CBIR system. Proposed method outperforms the retrieval rate from 75.9% using LTrP to 97.9% using proposed method on Corel 1K database. Improvement in performance measures as compared to the other existing methods evidence that, the proposed spatio-frequency local descriptor is more robust for image retrieval task.