A Survey on Different Video Steganography Techniques

Jenifer, J; Ratna, Silvia; Loret, J. B. Shajilin; Gethsy, D. Merlin

doi:10.1109/icoei.2018.8553847

“…Access to video processing software is a major factor in the rise of videos as a means of online communication. Using video steganography, data may be hidden in a video while leaving the video's visual quality intact 4 . The statistical analysis of visual characteristics and the temporal analysis of motion information have been proposed as robust methodologies.…”

Section: Figure 1 Different Kinds Of Cover Mediums For Steganographymentioning

confidence: 99%

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

Jebur

¹

,

Joda

²

,

Naser

³

2023

Baghdad Sci.J

0

View full text Add to dashboard Cite

Video steganography has become a popular option for protecting secret data from hacking attempts and common attacks on the internet. However, when the whole video frame(s) are used to embed secret data, this may lead to visual distortion. This work is an attempt to hide sensitive secret image inside the moving objects in a video based on separating the object from the background of the frame, selecting and arranging them according to object's size for embedding secret image. The XOR technique is used with reverse bits between the secret image bits and the detected moving object bits for embedding. The proposed method provides more security and imperceptibility as the moving objects are used for embedding, so it is difficult to notice the changes in the moving objects instead of using background area for embedding in the video. The experimental results showed the better visual quality of the stego video with PSNR values exceeding 58 dB, this indicates that the proposed method works without causing much distortion in the original video and transmitted secret message.

show abstract

“…To measure the robustness of the proposed approach, two measures were used, namely the Normalized Correlation (NC) and the Bit Error Rate (BER). NC is used to measure the similarity between original and extracted secret image [28,29]. NC is calculated as follows: (11) Whereas BER is used to measure the error rate between original and extracted secret image.…”

Section:  Evaluationmentioning

confidence: 99%

Video Steganography Technique Based on Enhanced Moving Objects Detection Method

Jebur

¹

,

Joda

²

,

Naser

³

2023

JUBPAS

0

View full text Add to dashboard Cite

Video steganography has become a popular option for protecting secret data from hacking attempts and common attacks on the internet. However, when the whole video frame(s) are used to embed secret data, this may lead to visual distortion. Materials and Methods: This work is an attempt to hide sensitive secret image inside the moving objects in a video based on separating the object from the background of the frame, selecting and arranging them according to object's size for embedding secret image. The proposed approach reverses the secret image bits and uses XOR technique between the reversed bits and the detected moving object bits for embedding. The proposed approach provides more security and imperceptibility as the moving objects are used for embedding, so it is difficult to notice the changes in the moving objects instead of using background area for embedding in the video. Further development to the proposed approach in the area of video steganography has been done by applying spatial model in combination with statistical model. Additional LSB styles have been also applied to evaluate the ability of the proposed approach in detecting moving objects. In addition to evaluating the robustness of the proposed approach against different attacks such as salt and pepper noise and median filtering. Results: The experimental results showed the better visual quality of the stego video with PSNR values exceeding 70 dB, this indicates that the proposed method works without causing much distortion in the original video and transmitted secret message. Conclusion: The experimental proof of the proposed approach can successfully detect and embed secret image. Also, it provides more security and imperceptibility as the data was hidden in the moving objects and the updates in the moving objects are difficult to notice rather than the static region in a video

show abstract

“…Maximizing the embedding rate for a given undetectability is fundamental to any steganographic algorithm. Multimedia objects are excellent choices for steganography due to their rich structural features and their ubiquity in business and daily life [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Information-Theoretic Bounds for Steganography in Multimedia

Mohamed¹,

Abdelaziz²,

Elliethy³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

<p>Steganography in multimedia aims to embed secret data into an innocent looking multimedia cover object. This embedding introduces some distortion to the cover object and produces a corresponding stego object. The embedding distortion is measured by a cost function that determines the detection probability of the existence of the embedded secret data. A cost function related to the maximum embedding rate is typically employed to evaluate a steganographic system. In addition, the distribution of multimedia sources follows the Gibbs distribution which is a complex statistical model that restricts analysis. Thus, previous multimedia steganographic approaches either assume a relaxed distribution or presume a proposition on the maximum embedding rate and then try to prove it is correct. Conversely, this paper introduces an analytic approach to determining the maximum embedding rate in multimedia cover objects through a constrained optimization problem concerning the relationship between the maximum embedding rate and the probability of detection by any steganographic detector. The KL-divergence between the distributions for the cover and stego objects is used as the cost function as it upper bounds the performance of the optimal steganographic detector. An equivalence between the Gibbs and correlated-multivariate-quantized-Gaussian distributions is established to solve this optimization problem. The solution provides an analytic form for the maximum embedding rate in terms of the WrightOmega function. Moreover, it is proven that the maximum embedding rate is in agreement with the commonly used Square Root Law (SRL) for steganography, but the solution presented here is more accurate. Finally, the theoretical results obtained are verified experimentally.</p>

show abstract

A Survey on Different Video Steganography Techniques

Cited by 9 publications

References 16 publications

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

Video Steganography Technique Based on Enhanced Moving Objects Detection Method

Information-Theoretic Bounds for Steganography in Multimedia

Contact Info

Product

Resources

About