An analysis of quality factor on image steganalysis (original) (raw)
Related papers
Steganalysis using image quality metrics
Image Processing, IEEE …, 2003
We present techniques for steganalysis of images that have been potentially subjected to steganographic algorithms, both within the passive warden and active warden frameworks. Our hypothesis is that steganographic schemes leave statistical evidence that can be exploited for detection with the aid of image quality features and multivariate regression analysis. To this effect image quality metrics have been identified based on the analysis of variance (ANOVA) technique as feature sets to distinguish between cover-images and stego-images. The classifier between cover and stego-images is built using multivariate regression on the selected quality metrics and is trained based on an estimate of the original image. Simulation results with the chosen feature set and wellknown watermarking and steganographic techniques indicate that our approach is able with reasonable accuracy to distinguish between cover and stego images.
Performance Evaluation of Different Universal Steganalysis Techniques in JPG Files
Annales UMCS, Informatica, 2012
Steganalysis is the art of detecting the presence of hidden data in files. In the last few years, there have been a lot of methods provided for steganalysis. Each method gives a good result depending on the hiding method. This paper aims at the evaluation of five universal steganalysis techniques which are "Wavelet based steganalysis", "Feature Based Steganalysis", "Moments of characteristic function using wavelet decomposition based steganalysis", "Empirical Transition Matrix in DCT Domain based steganalysis", and "Statistical Moment using jpeg2D array and 2D characteristic function". A large Dataset of Images-1000 images-are subjected to three types of steganographic techniques which are "Outguess", "F5" and "Model Based" with the embedding rate of 0.05, 0.1, and 0.2. It was followed by extracting the steganalysis feature used by each steganalysis technique for the stego images as well as the cover image. Then half of the images are devoted to train the classifier. The Support vector machine with a linear kernel is used in this study. The trained classifier is then used to test the other half of images, and the reading is reported The "Empirical Transition Matrix in DCT Domain based steganalysis" achieves the highest values among all the properties measured and it becomes the first choice for the universal steganalysis technique. Pobrane z czasopisma Annales AI-Informatica http://ai.annales.umcs.pl
In this paper, we introduce a new feature-based steganalytic method for JPEG images and use it as a benchmark for comparing JPEG steganographic algorithms and evaluating their embedding mechanisms. The detection method is a linear classifier trained on feature vectors corresponding to cover and stego images. In contrast to previous blind approaches, the features are calculated as an L 1 norm of the difference between a specific macroscopic functional calculated from the stego image and the same functional obtained from a decompressed, cropped, and recompressed stego image. The functionals are built from marginal and joint statistics of DCT coefficients. Because the features are calculated directly from DCT coefficients, conclusions can be drawn about the impact of embedding modifications on detectability. Three different steganographic paradigms are tested and compared. Experimental results reveal new facts about current steganographic methods for JPEGs and new design principles for more secure JPEG steganography. system is considered broken. For a more exact treatment of the concept of steganographic security, the reader is referred to [1,2].
JPEG Steganalysis Using HBCL Statistics and FR Index
Intelligence and Security Informatics, 2010
Blind Steganalysis attempts to detect steganographic data without prior knowledge of either the embedding algorithm or the 'cover' image. This paper proposes new features for JPEG blind steganalysis using a combination of Huffman Bit Code Length (HBCL) Statistics and File size to Resolution ratio (FR Index); the Huffman Bit File Index Resolution (HUBFIRE) algorithm proposed uses these functionals to build the classifier using a multi-class Support Vector Machine (SVM). JPEG images spanning a wide range of resolutions are used to create a 'stego-image' database employing three embedding schemes-the advanced Least Significant Bit encoding technique, that embeds in the spatial domain, a transform-domain embedding scheme: JPEG Hide-and-Seek and Model Based Steganography which employs an adaptive embedding technique. This work employs a multi-class SVM over the proposed 'HUBFIRE' algorithm for statistical steganalysis, which is not yet explored by steganalysts. Experiments conducted prove the model's accuracy over a wide range of payloads and embedding schemes.
Steganalysis of JPEG Images with Joint Transform Features
Lecture Notes in Computer Science, 2009
In this paper, a universal steganalysis scheme for JPEG images based upon joint transform features is presented. We first analyzed two different transform domains (Discrete Cosine Transform and Discrete Wavelet Transform) separately, to extract features for steganalysis. Then a combination of these two feature sets is constructed and employed for steganalysis. A Fisher Linear Discriminant classifier is trained on features from both clean and steganographic images using all three feature sets and subsequently used for classification. Experiments performed on images embedded with two variants of F5 and Model based steganographic techniques reveal the effectiveness of proposed steganalysis approach by demonstrating improved detection for joint features.
Neighboring joint density-based JPEG steganalysis
ACM Transactions on Intelligent Systems and Technology, 2011
The threat posed by hackers, spies, terrorists, and criminals, etc. using steganography for stealthy communications and other illegal purposes is a serious concern of cyber security. Several steganographic systems that have been developed and made readily available utilize JPEG images as carriers. Due to the popularity of JPEG images on the Internet, effective steganalysis techniques are called for to counter the threat of JPEG steganography. In this article, we propose a new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images. First, neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array and the absolute array, respectively; then a support vector machine (SVM) is applied to the features for detection. An evolving neural-fuzzy inference system is employed to predict the hiding amount in JPEG steganograms. We also adopt a feature selection method of support vector machine recursive feature elimination to reduce the number of features. Experimental results show that, in detecting several JPEG-based steganographic systems, our method prominently outperforms the well-known Markov-process based approach.
Improvements of Steganography Parameter in Binary Images and JPEG Images against Steganalysis.
International Journal of Engineering Sciences & Research Technology, 2013
Steganography is a science of hiding messages into multimedia documents. A message can be hidden in a document only if the content of a document has high redundancy. Although the embedded message changes the characteristics and nature of the document, it is required that these changes are difficult to be identified by an unsuspecting user. On the other hand, steganalysis develops theories, methods and techniques that can be used to detect hidden messages in multimedia documents. The documents without any hidden messages are called cover documents and the documents with hidden messages are named stego documents. The work of this research paper concentrates on image steganalysis. We present four different types of steganalysis techniques. These steganalysis techniques are developed to counteract the steganographic methods that use binary (black and white) images as the cover media. Unlike grayscale and color images, binary images have a rather modest statistical nature. This makes it difficult to apply directly the existing steganalysis on binary images.
Quantitative and Binary Steganalysis in JPEG: A Comparative Study
2018 26th European Signal Processing Conference (EUSIPCO), 2018
We consider the problem of steganalysis, in which Eve (the steganalyst) aims to identify a steganographer, Alice who sends images through a network. We can also hypothesise that Eve does not know how many bits Alice embed in an image. In this paper, we investigate two different steganalysis scenarios: Binary Steganalysis and Quantitative Steganalysis. We compare two classical steganalysis algorithms from the state-of-the-art: the QS algorithm and the GLRT-Ensemble Classifier, with features extracted from JPEG images obtained from BOSSbase 1.01. As their outputs are different, we propose a methodology to compare them. Numerical results with a state-of-the-art Content Adaptive Embedding Scheme and a Rich Model show that the approach of the GLRT-ensemble is better than the QS approach when doing Binary Steganalysis but worse when doing Quantitative Steganalysis.
Performance study of common image steganography and steganalysis techniques
Journal of Electronic Imaging, 2006
We investigate the performance of state of the art universal steganalyzers proposed in the literature. These universal steganalyzers are tested against a number of well-known steganographic embedding techniques that operate in both the spatial and transform domains. Our experiments are performed using a large data set of JPEG images obtained by randomly crawling a set of publicly available websites. The image data set is categorized with respect to size, quality, and texture to determine their potential impact on steganalysis performance. To establish a comparative evaluation of techniques, undetectability results are obtained at various embedding rates. In addition to variation in cover image properties, our comparison also takes into consideration different message length definitions and computational complexity issues. Our results indicate that the performance of steganalysis techniques is affected by the JPEG quality factor, and JPEG recompression artifacts serve as a source of confusion for almost all steganalysis techniques. © 2006 SPIE and IS&T.
STEGANALYSIS ON IMAGES BASED ON THE CLASSIFICATION OF IMAGE FEATURE SETS USING SVM CLASSIFIER
The two popular schemes used for image steganography are spatial domain embedding and transform domain embedding. Most of the steganographic techniques either use spatial domain or transform domain to embed the secret message. This work is about attack on Modern spatial domain image steganography. The previous work evaluates the performance of five state of the art content-adaptive steganographic techniques. Since WOW is believed to be a strong steganographic method which will with stand against attacks, this work, does steganalysis on WOW stego images. This paper attempts to detect the stego images created by WOW algorithm by using Chen Feature set, Subtractive Pixel Adjacency Mode (SPAM) Feature set and Ccpev Feature set. It uses a SVM based classifier to detect the stego images.