MATLAB CODE FOR TAMPERING DETECTION

TAMPERING DETECTION USING WATERMARKING

                   We  presents a method to detect video tampering and distinguish it from common video processing operations, such as recompression, noise, and brightness increase, using a practical watermarking scheme for real-time authentication of digital video. In our method, the watermark signals represent the macro block's and frame’s indices,  and are embedded into the nonzero quantized discrete cosine transform value of blocks, mostly the last nonzero values,  enabling our method to detect spatial, temporal, and spatiotemporal tampering. Our method can be easily configured to adjust transparency, robustness, and capacity of the system according to the specific application at hand. In addition, our method takes advantage of content-based cryptography and increases the security of the system. 

                                             

                                       Fig.Embedding and detecting flow chart

                    Tamper Detection is the ability of a device to sense that an active attempt to compromise the device integrity or the data associated with the device is in progress; the detection of the threat may enable the device to initiate appropriate defensive actions.

FOR LATEST IEEE PROJECTS

                                        click here

MATLAB CODE FOR DATA HIDING AND COMPRESSION

DATA-HIDING AND COMPRESSION SCHEME 

               

                       We propose a novel joint data-hiding and compression scheme for digital images using side match vector quantization SMVQ) and image inpainting. The two functions of data hiding and image compression can be integrated into one single module seamlessly. On the sender side, except for the blocks in the leftmost and topmost of the image, each of the other residual blocks in raster-scanning order can be embedded with secret data and compressed simultaneously by SMVQ or image inpainting adaptively according to the current embedding bit. Vector quantization is also utilized for some complex blocks to control the visual distortion and error diffusion caused by the progressive compression. After segmenting the image compressed codes into a series of sections by the indicator bits, the receiver can achieve the extraction of secret bits and image decompression successfully according to the index values in the segmented sections.

                                                   

                                                Fig.Original and Encrypted image

                    (SMVQ) was designed as an improved version of VQ, in which both the codebook and the sub codebooks are used to generate the index values, excluding the blocks in the leftmost column and the topmost row.

FOR LATEST IEEE PROJECTS

                                                        click here 

MATLAB CODE FOR DETECTION OF LICENSE PLATE NUMBER

LOCALIZATION OF LICENSEPLATE NUMBER

                   The design of a new genetic algorithm (GA) is introduced to detect the locations of license plate (LP) symbols. An adaptive threshold method is applied to overcome the dynamic changes of illumination conditions when converting the image into binary. Connected component analysis technique (CCAT) is used to detect candidate objects inside the unknown image. A scale-invariant geometric relationship matrix is introduced to model the layout of symbols in any LP that simplifies system adaptability when applied in different countries. Moreover, two new crossover operators, based on sorting, are introduced, which greatly improve the convergence speed of the system.

                           Fig.Detected license plate number 

                            Most of the CCAT problems, such as touching or broken bodies, are minimized by modifying the GA to perform partial match until reaching an acceptable fitness value. The system is implemented using MATLAB and various image samples are experimented with to verify the distinction of the proposed system.

 FOR LATEST IEEE PROJECTS

                                                           click here

MATLAB CODE FOR CONVERT RGB TO BINARY IMAGE

Query=> How to convert colour  image into binary image based on Threshold using MATLAB?...

Ans=> To convert colour image into binary image we have to use the syntax “im2bw”...An example is given below for reference...

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

b=im2bw(a,0.3);                          %coverting to binary based on Th value

figure,                                          %opening figure window

subplot(1,2,1),subimage(a);        %display colour image

subplot(1,2,2),subimage(b);        %display binary image

FOR MORE DETAILS

                                  CLICK HERE

MATLAB CODE FOR CBIR

CONTENT BASED IMAGE RETRIEVAL 

          "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness. Having humans manually annotate images by entering keywords or metadata in a large database can be time consuming and may not capture the keywords desired to describe the image. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. In the same regard, CBIR systems have similar challenges in defining success.

                                     Figure. An example of Image retrieval operation

Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval(CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based image retrieval is opposed to traditional concept-based approaches 

FOR MORE DETAILS

                     click here

MATLAB CODE FOR CONVERT RGB TO GRAY IMAGE

Query=> How to convert colour  image into gray image using MATLAB?...

Ans=> To convert colour image into gray image we have to use the syntax “rgb2gray”...An example is given below for reference...

a=imread('peppers.png');          %reading an image

b=rgb2gray(a);                          %coverting to gray image

figure,                                        %opening figure window

subplot(1,2,1),subimage(a);title(‘colour’);        %display colour image

subplot(1,2,2),subimage(b);title(‘gray’);           %display gray image

FOR MORE DETAILS

                                 click here

MATLAB CODE FOR CANCER DETECTION

PULMONARY NODULES

In general, a “pulmonary nodule” is a small, roundish growth on the lung that measures three centimeters in diameter or less. If the growth is larger than that, it is called a “pulmonary mass.” While pulmonary nodules may grow to become a pulmonary mass, some nodules may not grow at all. There are many causes of pulmonary nodules. These include infection, such as fungal or bacterial infections, noncancerous processes, such as sarcoidosis, or cancerous processes, such as lung cancer, lymphoma, or metastatic cancer from other organs. The likelihood that a pulmonary nodule represents lung cancer depends upon three major factors, your age, your smoking history, and your environmental exposure history. Generally, less than 10 percent of pulmonary nodules turn out to be lung cancer.

                                                 Figure.  CT scan image with a Pulmonary nodule

SYMPTOMS OF PULMONARY NODULES

Because pulmonary nodules are small, they rarely cause any symptoms. Some patients might experience symptoms of a respiratory infection, such as a symptoms associated with chest colds or mild flu. Most pulmonary nodules are discovered by accident, when a patient gets a chest X-ray or a CT scan performed for another purpose. 

EVALUATING A PULMONARY NODULE

The immediate goal of evaluating a pulmonary nodule is determining the cancerous potential of the nodule. This is first done with a thorough evaluation of the personal and medical history, the environmental exposure history, and the chest CT scan. If a nodule is determined to have significant cancer potential and is one centimeter in diameter or greater, diagnostic procedures are used to determine the cause of the pulmonary nodule. There are many approaches to evaluating and diagnosing pulmonary nodules that do not require surgery, such as PET scans, bronchoscopy, endobronchial ultrasound, CT-guided needle biopsy, and fluoroscopically guided biopsy. When the pulmonary nodule cannot be diagnosed using these noninvasive approaches, surgical approaches are considered, such as video-assisted thorocoscopic surgery, a mini-thoracotomy, or a thoracotomy. Once the cause of the pulmonary nodule has been determined, an appropriate treatment plan tailored to the disease can be assembled.

FOLLOWING A PULMONARY NODULE

       The majority of pulmonary nodules are extremely small, less than one centimeter in diameter. Unfortunately, these pulmonary nodules are too small to be diagnosed safely and accurately using any of the currently available procedures or tests. Because these very small pulmonary nodules can represent early lung cancer, they need to be followed closely using CT scans with a well developed algorithm for evaluating whether the pulmonary nodule has grown over time. If the size of these pulmonary nodules remains unchanged for two years, the likelihood of these pulmonary nodules representing lung cancer is very small.

 FOR MORE DETAILS

                           click here

MATLAB CODE FOR IMAGE COPY-MOVE FORGERY DETECTION

SEGMENTATION-BASED IMAGE COPY-MOVE FORGERY DETECTION SCHEME

                       An image with copy-move forgery (CMF) contains at least a couple of regions whose contents are identical. CMF may be performed by a forger aiming either to cover the truth or to enhance the visual effect of the image. Normal people might neglect this malicious operation when the forger deliberately hides the tampering trace. So we are in urgent need of an effective CMF detection (CMFD) method to automatically point out the clone regions in the image. And CMFD is becoming one of the most important and popular digital forensic techniques currently.

       Figure. a)Original Image,              b)Copy-move Forgery Image         c) Detection of CMF region       

         Digital images are easy to manipulate and edit due to availability of powerful image processing and editing software. Nowadays, it is possible to add or remove important features from an image without leaving any obvious traces of tampering. As digital cameras and video cameras replace their analog counterparts, the need for authenticating digital images, validating their content and detecting forgeries will only increase.

FOR MORE DETAILS

                             click here

MATLAB CODE FOR IMAGE COMPLEMENT

Query=> How to convert original  image into complement image using MATLAB?...

Ans=> To convert original image into complement image we have to use the syntax “imcomplement”...An example is given below for reference...

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

b=imcomplement(a);                  %taking complement

figure,                                         %opening figure window

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

subplot(1,2,2),subimage(b);title('complement image');      %display gray image

 FOR MORE DETAILS

                                      click here

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