MATLAB CODING FOR DATA HIDING

REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES

STEGANOGRAPHY

        Steganography is the art of hiding a secret message behind the normal message. This is used to transfer some secret message to other person and no interim person will be able to know what the real message which you wanted to convey was. This art of hiding secret messages has been used for years in real life communications. Since the evolvement of digital communication, it has also been used in digital images.

            

Figure. operation of reversible data hiding is shown in the above image

                 Reversible data hiding is the technique used to hide a secret message in an encrypted image and retrieving it without any loss in the data. The image is encrypted and a binary data bit is embedded in the encrypted image and transmitted while at the receiving end using the key we can decrypt the image and retrieve the binary data.

         FOR MORE DETAILS 

                                       CLICK HERE

MATLAB CODE FOR EARTHQUAKE TRIGGERED ROOF HOLES

DETECTION OF EARTHQUAKE TRIGGERED ROOF HOLES

An earthquake (also known as a quake, tremor or temblor) is the perceptible shaking of the surface of the Earth, which can be violent enough to destroy major buildings and kill thousands of people. The severity of the shaking can range from barely felt to violent enough to toss people around. Earthquakes have destroyed whole cities. They result from the sudden release of energy in the Earth's crust that creates seismic waves. The seismicity, seismism or seismic activity of an area refers to the frequency, type and size of earthquakes experienced over a period of time.

                          Figure., (a),(b) Detected roof holes

Many methods have been developed to detect damaged buildings due to earthquake. However, little attention has been paid to analyze slightly affected buildings. An unsupervised method is presented to detect earthquake-triggered “roof-holes” on rural houses from unmanned aerial vehicle (UAV) images. First, both orthomosaic and gradient images are generated from a set of UAV images. Then, a modified Chinese restaurant franchise model is used to learn an unsupervised model of the geo-object classes in the area by fusing both over segmented orthomosaic and gradient images. Finally, “roof-holes” on rural houses are detected using the learned model.

FOR MORE DETAILS

                            click here

HISTOGRAM EQUALIZATION MATLAB CODE

Query=> How to enhance contrast of image using histogram equalization in MATLAB?...

Ans=> To enhance contrast using histogram equalization we have to use the syntax “histeq”...An example is given below for reference...

a=imread('cameraman.tif');        %reading an image

b=histeq(a);                      %taking complement

figure,                           %opening figure window

subplot(1,2,1),subimage(a);title('original image');              %display gray image

subplot(1,2,2),subimage(b);title('hist equalized image');        %display histeq 

 image

 FOR MORE DETAILS

                  click here

MATLAB CODE FOR DILATION

Query=> How to dilate binary image with structuring element in MATLAB?...

Ans=> To dilate binary image with structuring element we have to use the syntax “imdilate”...An example is given below for reference...

bw = imread('text.png');            %reading an image

se = strel('line',11,90);               %structure element

bw2 = imdilate(bw,se);              %dilate process

figure,                                        %opening figure window

subplot(1,2,1),subimage(bw);title('original image');        %display gray image

subplot(1,2,2),subimage(bw2);title('dilated image');        %display dilated image

FOR MORE DETAILS

                                          click here

MATLAB CODE FACE RECOGNITION FROM BLUR, ILLUMINATION, AND POSE

FACE RECOGNITION FROM  BLUR, ILLUMINATION, AND POSE

             Existing methods for performing face recognition in the presence of blur are based on the convolution model and cannot handle non-uniform blurring situations that frequently arise from tilts and rotations in hand-held cameras. In this paper, we propose a methodology for face recognition in the presence of space-varying motion blur comprising of arbitrarily-shaped kernels. We model the blurred face as a convex combination of geometrically transformed instances of the focused gallery face, and show that the set of all images obtained by non-uniformly blurring a given image forms a convex set.

Fig:The gallery images ,illumination, facial expressions changes,small occlusions and differences in pose 

FOR LATEST IEEE PROJECTS  

                                                                                       CLICK HERE

MATLAB CODE FOR EROSION

Query=> How to erode binary image with structuring element in MATLAB?...

Ans=> To erode binary image with structuring element we have to use the syntax “imdilate”...An example is given below for reference...

originalBW = imread('circles.png');           %reading an image

se = strel('disk',11);                                     %structufre element

erodedBW = imerode(originalBW,se);       %erode process

figure,                                                          %opening figure window

subplot(1,2,1),subimage(originalBW);title('original image');   %display gray image

subplot(1,2,2),subimage(erodedBW);title('dilated image');      %display eroded image

FOR MORE DETAILS

                                     click here

MATLAB CODE FOR SPOOFING DETECTION

SPOOFING DETECTION OF IRIS,FACE, AND FINGERPRINT

              Three relevant modalities in which spoofing detection has been investigated are iris, face, and fingerprint. Benchmarks across these modalities usually share the common characteristic of being image- or video-based. In the context of irises, attacks are normally performed using printed iris images or, more interestingly, cosmetic contact lenses. With faces, impostors can present to the acquisition sensor a photography, a digital video, or even a 3D mask  of a valid user. For fingerprints, the most common spoofing method consists of using artificial replicas created in a cooperative way, where a mold of the fingerprint is acquired with the cooperation of a valid user and is used to replicate the user’s fingerprint with different materials, including gelatin, latex, play-doh or silicone. 

Fig: Real and Fake Finger Print

FOR MORE DETAILS 

                                                                                                 CLICK HERE