Biblio

This paper proposes an advanced scheme of message security in 3D cover images using multiple layers of security. Cryptography using AES-256 is implemented in the first layer. In the second layer, edge detection is applied. Finally, LSB steganography is executed in the third layer. The efficiency of the proposed scheme is measured using a number of performance metrics. For instance, mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean absolute error (MAE) and entropy.

Hiding information is a process to hide data or include it in different digital media such as image, audio, video, and text. However, there are many techniques to achieve the process of hiding information in the image processing, in this paper, a new method has been proposed for hidden data mechanism (which is a text file), then a transposition cipher method has been employed for encryption completed. It can be used to build an encrypted text and also to increase security against possible attacks while sending it over the World Wide Web. A genetic algorithm has been affected in the adjustment of the encoded text and DNA in the creation of an encrypted text that is difficult to detect and then include in the image and that affected the image visual quality. The proposed method outperforms the state of arts in terms of efficiently retrieving the embedded messages. Performance evaluation has been recorded high visual quality scores for the (SNR (single to noise ratio), PSNR (peak single to noise ratio) and MSE (mean square error).

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into a digital video. The video content is dynamic in nature and this makes the detection of hidden data difficult than other steganographic techniques. The main motive of using video steganography is that the videos can store large amount of data in it. This paper focuses on security using the combination of hybrid neural networks and hash function for determining the best bits in the cover video to embed the secret data. For the embedding process, the cover video and the data to be hidden is uploaded. Then the hash algorithm and neural networks are applied to form the stego video. For the extraction process, the reverse process is applied and the secret data is obtained. All experiments are done using MatLab2016a software.

Least Significant Bit (LSB) as one of steganography methods that already exist today is really mainstream because easy to use, but has weakness that is too easy to decode the hidden message. It is because in LSB the message embedded evenly to all pixels of an image. This paper introduce a method of steganography that combine LSB with clustering method that is Fuzzy C-Means (FCM). It is abbreviated with LSB\_FCM, then compare the stegano result with LSB method. Each image will divided into two cluster, then the biggest cluster capacity will be choosen, finally save the cluster coordinate key as place for embedded message. The key as a reference when decode the message. Each image has their own cluster capacity key. LSB\_FCM has disadvantage that is limited place to embedded message, but it also has advantages compare with LSB that is LSB\_FCM have more difficulty level when decrypted the message than LSB method, because in LSB\_FCM the messages embedded randomly in the best cluster pixel of an image, so to decrypted people must have the cluster coordinate key of the image. Evaluation result show that the MSE and PSNR value of LSB\_FCM some similiar with the pure LSB, it means that LSB\_FCM can give imperceptible image as good as the pure LSB, but have better security from the embedding place.

Image encryption takes been used by armies and governments to help top-secret communication. Nowadays, this one is frequently used for guarding info among various civilian systems. To perform secure image encryption by means of various chaotic maps, in such system a legal party may perhaps decrypt the image with the support of encryption key. This reversible chaotic encryption technique makes use of Arnold's cat map, in which pixel shuffling offers mystifying the image pixels based on the number of iterations decided by the authorized image owner. This is followed by other chaotic encryption techniques such as Logistic map and Tent map, which ensures secure image encryption. The simulation result shows the planned system achieves better NPCR, UACI, MSE and PSNR respectively.

Multimedia security and copyright protection has been a popular topic for research and application, due to the explosion of data exchange over the internet and the widespread use of digital media. Watermarking is a process of hiding the digital information inside a digital media. Information hiding as digital watermarks in multimedia enables protection mechanism in decrypted contents. This paper presents a comparative study of existing technique used for digital watermarking an image using Genetic Algorithm and Bacterial Foraging Algorithm (BFO) based optimization technique with proposed one which consists of Genetic Algorithm and Honey Bee based optimization technique. The results obtained after experiment conclude that, new method has indeed outperformed then the conventional technique. The implementation is done over the MATLAB.

This paper investigates several techniques that increase the accuracy of motion boundaries in estimated motion fields of a local dense estimation scheme. In particular, we examine two matching metrics, one is MSE in the image domain and the other one is a recently proposed multiresolution metric that has been shown to produce more accurate motion boundaries. We also examine several different edge-preserving filters. The edge-aware moving average filter, proposed in this paper, takes an input image and the result of an edge detection algorithm, and outputs an image that is smooth except at the detected edges. Compared to the adoption of edge-preserving filters, we find that matching metrics play a more important role in estimating accurate and compressible motion fields. Nevertheless, the proposed filter may provide further improvements in the accuracy of the motion boundaries. These findings can be very useful for a number of recently proposed scalable interactive video coding schemes.

Salt and Pepper Noise is very common during transmission of images through a noisy channel or due to impairment in camera sensor module. For noise removal, methods have been proposed in literature, with two stage cascade various configuration. These methods, can remove low density impulse noise, are not suited for high density noise in terms of visible performance. We propose an efficient method for removal of high as well as low density impulse noise. Our approach is based on novel extension over iterated conditional modes (ICM). It is cascade configuration of two stages - noise detection and noise removal. Noise detection process is a combination of iterative decision based approach, while noise removal process is based on iterative noisy pixel estimation. Using improvised approach, up to 95% corrupted image have been recovered with good results, while 98% corrupted image have been recovered with quite satisfactory results. To benchmark the image quality, we have considered various metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and SSIM (Structure Similarity Index Measure).