Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames

The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab.

Reversible Video Steganography Using Quick Response Codes and Modified ElGamal Cryptosystem

The rapid transmission of multimedia information has been achieved mainly by recent advancements in the Internet’s speed and information technology.In spite of this,advancements in technology have resulted in breaches of privacy and data security.When it comes to protecting private information in today’s Internet era,digital steganography is vital.Many academics are interested in digital video because it has a great capability for concealing important data.There have been a vast number of video steganography solutions developed lately to guard against the theft of confidential data.The visual imperceptibility,robustness,and embedding capacity of these approaches are all challenges that must be addressed.In this paper,a novel solution to reversible video steganography based on Discrete Wavelet Transform(DWT)and Quick Response(QR)codes is proposed to address these concerns.In order to increase the security level of the suggested method,an enhanced ElGamal cryptosystem has also been proposed.Prior to the embedding stage,the suggested method uses the modified ElGamal algorithm to encrypt secret QR codes.Concurrently,it applies two-dimensional DWT on the Y-component of each video frame resulting in Approximation(LL),Horizontal(LH),Vertical(HL),and Diagonal(HH)sub-bands.Then,the encrypted Low(L),Medium(M),Quantile(Q),and High(H)QR codes are embedded into the HL sub-band,HHsub-band,U-component,and V-component of video frames,respectively,using the Least Significant Bit(LSB)technique.As a consequence of extensive testing of the approach,it was shown to be very secure and highly invisible,as well as highly resistant to attacks from Salt&Pepper,Gaussian,Poisson,and Speckle noises,which has an average Structural Similarity Index(SSIM)of more than 0.91.Aside from visual imperceptibility,the suggested method exceeds current methods in terms of Peak Signal-to-Noise Ratio(PSNR)average of 52.143 dB,and embedding capacity 1 bpp.

Optimizing Region of Interest Selection for Effective Embedding in Video Steganography Based on Genetic Algorithms

With the widespread use of the internet,there is an increasing need to ensure the security and privacy of transmitted data.This has led to an intensified focus on the study of video steganography,which is a technique that hides data within a video cover to avoid detection.The effectiveness of any steganography method depends on its ability to embed data without altering the original video’s quality while maintaining high efficiency.This paper proposes a new method to video steganography,which involves utilizing a Genetic Algorithm(GA)for identifying the Region of Interest(ROI)in the cover video.The ROI is the area in the video that is the most suitable for data embedding.The secret data is encrypted using the Advanced Encryption Standard(AES),which is a widely accepted encryption standard,before being embedded into the cover video,utilizing up to 10%of the cover video.This process ensures the security and confidentiality of the embedded data.The performance metrics for assessing the proposed method are the Peak Signalto-Noise Ratio(PSNR)and the encoding and decoding time.The results show that the proposed method has a high embedding capacity and efficiency,with a PSNR ranging between 64 and 75 dBs,which indicates that the embedded data is almost indistinguishable from the original video.Additionally,the method can encode and decode data quickly,making it efficient for real-time applications.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

Novel Video Steganography Algorithm Based on Secret Sharing and Error-Correcting Code for H.264/AVC

In this paper, we analyze the video steganography technique, which is used to ensure national security and the confidentiality of the information of governmental agencies and enterprises. Videos may be used to transmit secrets and conduct covert communication. As such, we present an algorithm based on a secret sharing scheme and an Error-Correcting Code（ECC）, which combines Grey Relational Analysis（GRA） with a partition mode in video compression standard H.264/AVC. First, we process secret information by secret sharing, and then use an ECC to process the obtained information. Moreover, we choose the Discrete Cosine Transform（DCT） blocks using GRA, and then use rules to hide the pretreated information in DCT coefficients of the video frames. Experimental results indicate that our algorithm has good invisibility, better robustness, good anti-steganalysis ability, and little influence on the bit rate of the video carrier. In addition, the bit error rate is low after attacks such as noise, filtering,or frame loss in the simulation environment.

A MV-Based Steganographic Algorithm for H.264/AVC without Distortion

H.264/AVC video is one of the most popular multimedia and has been widely used as the carriers of video steganography.In this paper,a novel motion vector(MV)based steganographic algorithm is proposed for the H.264/AVC compressed video without distortion.Four modules are introduced to eliminate the distortion caused by the modifications of motion vectors and guarantee the security of the algorithm.In the embedding block,the motion vector space encoding is used to embed a(2n+1)-ary notational number into an n-dimension vector composed of motion vectors generated from the selection block.Scrambling is adopted to disturb the order of steganographic carriers to improve the randomness of the carrier before the operation of embedding.The re-motion compensation(re-MC)block will re-construct the macroblock(MB)whose motion vectors have been modified by embedding block.System block plays the role of the generator for chaotic sequences and encryptor for secret data.Experimental results demonstrate that our proposed algorithm can achieve high embedding capacity without stego video visual quality distortion,it also presents good undetectability for existing MV-based steganalysis feature.Performance comparisons with other existing algorithms are provided to demonstrate the superiority of the proposed algorithm.