Biblio

Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.

Digital video has various types of entities, which are utilized as embedding domains to hide messages in steganography. However, nearly all video steganography uses only one type of embedding domain, resulting in limited embedding capacity and potential security risks. In this paper, we firstly propose to embed in multi-domains for video steganography by combining partition modes (PMs) and motion vectors (MVs). The multi-domain embedding (MDE) aims to spread the modifications to different embedding domains for achieving higher undetectability. The key issue of MDE is the interactions of entities across domains. To this end, we design two MDE strategies, which hide data in PM domain and MV domain by sequential embedding and simultaneous embedding respectively. These two strategies can be applied to existing steganography within a distortion-minimization framework. Experiments show that the MDE strategies achieve a significant improvement in security performance against targeted steganalysis and fusion based steganalysis.

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.

Now-a-days, video steganography has developed for a secured communication among various users. The two important factor of steganography method are embedding potency and embedding payload. Here, a Multiple Object Tracking (MOT) algorithmic programs used to detect motion object, also shows foreground mask. Discrete wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are used for message embedding and extraction stage. In existing system Least significant bit method was proposed. This technique of hiding data may lose some data after some file transformation. The suggested Multiple object tracking algorithm increases embedding and extraction speed, also protects secret message against various attackers.

The safety of information inside of cloud networks is of interest to the network administrators. In a new insider attack, inside attackers merge confidential information with videos using digital video steganography. The video can be uploaded to video websites, where the information can be distributed online, where it can cost firms millions in damages. Standard behavior based exfiltration detection does not always prevent these attacks. This form of steganography is almost invisible. Existing compressed video steganalysis only detects small-payload watermarks. We develop such a strategy using distributed algorithms and classify videos, then compare existing algorithms to new ones. We find our approach improves on behavior based exfiltration detection, and on the existing online video steganalysis.