The main goal of image steganography techniques is to maximize embedding rate while minimizing the change of the cover image after embedding. Much work has been done on how sender would embed the secret message in the cover image (i.e. embedding techniques) but there is a few works focus on how the senders choose the cover images. One advantage of image steganography is that the cover image only acts as a carrier for the message and the embedder (sender) has the freedom to choose a cover image amongst a set of cover images those results in the least detectable stego image. The way of choosing the cover image is important and since it is available to the sender both the cover and stego images, then the senders are able to measure the embedding artifacts directly. Thus, we are interested in measures which are able to quantify such artifacts. We can use the cover-stego based on measures which we have employed in our work to select best cover image among set of images. The measures used are (1) Number of Modifications to the cover image could be thought as the most intuitive. The smaller the number of changes made the less detectable the resulting stego image should be, (2) Peak Signal to Noise Ratio (PSNR) which is obtained from the cover-stego image pairs where higher PSNR values are assumed to be indicative of lesser delectability, or (3) Based on the robustness to the steganalysis techniques. For the experiments we used a dataset of gray scale images with size of 512×512 resolutions as a cover image with different secret message size from 0.2 to 1.0 bits per pixel.
Image Steganography is the technique of hiding sensitive data (secrete message) inside cover images in a way that no suspicion occurs to attackers, while steganalysis is the technique of detecting the embedded data by unauthorized persons. As a first step of detecting hidden data, distinguishing between original (Images without secrete message) and Stego (Images contain secrete message) is important. In this paper we design and propose a novel scheme based on the emerging field of Topological Data Analysis (TDA) concept of persistent homological (PH) invariants (e.g. No. of connected components), associated with certain image features. Selected group of Uniform Local Binary Pattern (LBP), which is a texture descriptor, codes representing the image features used to construct a sequence of simplicial complexes (SC) from an increasing sequence of distance thresholds (T). We calculate the corresponding non-increasing sequence of homological invariants which shows the speed at which the constructed sequence of SCs terminates. This approach is sensitive to differentiate original images from stego images. We test this approach on three different embedding techniques which are Traditional Least Significant Bits (TLSB) embedding technique, spatial Universal Wavelet Relative Distortion (S-UNIWARD) and LSB-Witness embedding technique together with a large number of images chosen randomly from large database of images. Preliminary results show that the PH sequence defines a discriminates criterion for steganalysis purpose with over 90% classification accuracy.
Image quality is a major factor influencing pattern recognition accuracy and help detect image tampering for forensics. We are concerned with investigating topological image texture analysis techniques to assess different type of degradation. We use Local Binary Pattern (LBP) as a texture feature descriptor. For any image construct simplicial complexes for selected groups of uniform LBP bins and calculate persistent homology invariants (e.g. number of connected components). We investigated image quality discriminating characteristics of these simplicial complexes by computing these models for a large dataset of face images that are affected by the presence of shadows as a result of variation in illumination conditions. Our tests demonstrate that for specific uniform LBP patterns, the number of connected component not only distinguish between different levels of shadow effects but also help detect the infected regions as well.
This paper is concerned with robust steganographic techniques to hide and communicate biometric data in mobile media
objects like images, over open networks. More specifically, the aim is to embed binarised features extracted using
discrete wavelet transforms and local binary patterns of face images as a secret message in an image. The need for such
techniques can arise in law enforcement, forensics, counter terrorism, internet/mobile banking and border control. What
differentiates this problem from normal information hiding techniques is the added requirement that there should be
minimal effect on face recognition accuracy.
We propose an LSB-Witness embedding technique in which the secret message is already present in the LSB plane but
instead of changing the cover image LSB values, the second LSB plane will be changed to stand as a witness/informer to
the receiver during message recovery. Although this approach may affect the stego quality, it is eliminating the weakness
of traditional LSB schemes that is exploited by steganalysis techniques for LSB, such as PoV and RS steganalysis, to
detect the existence of secrete message.
Experimental results show that the proposed method is robust against PoV and RS attacks compared to other variants of
LSB. We also discussed variants of this approach and determine capacity requirements for embedding face biometric
feature vectors while maintain accuracy of face recognition.
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