Beste Ustubioglu

Frame duplication/mirroring detection method with binary features

Multimedia devices have become increasingly popular due to high quality and low cost products using advanced technology. These devices can capture multimedia files, which can be modified easily by video editing tools. One of the most frequently encountered forgery types in video forensics is the frame duplication (FD) forgery. Many methods have been proposed in the literature to deal with this type of forgery. These methods do not consider frame-mirroring (FM) attack which copy a sequence of frames and paste its mirrored versions somewhere else on the same video. A new FD/FM detection method is proposed in this work. The method extracts binary features from frames and determines the similarity among features. Peak-signal-to-noise ratio of the candidate frames is used to eliminate some of the large number of candidates to improve the detection of the forged frames. Experimental results show that the proposed method successfully detects FM/FD attacks and also yields better execution time and detection results compared to similar works reported in the literature.

Medical Image Tamper Detection Based on Passive Image Authentication

Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient’s history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.

LBP-DCT Based Copy Move Forgery Detection Algorithm

Increase on the availability of the image editing software makes the forgery of the digital image easy. Researchers proposed methods to cope with image authentication in recent years. We proposed a passive image authentication technique to determine the copy move forgery. First, the method divides the image into overlapping blocks. It uses LBP (Local Binary Pattern) to label each block. Labeled blocks are transformed into frequency domain using DCT (Discrete Cosine Transform). Sign values of the first fifteen coefficients of the zigzag scanned block plus average Y, Cb, Cr values constitutes the feature vector for the block. Finally, the feature vectors are lexicographically sorted and element-by-element similarity measurement is used to determine the forged blocks. Experimental results show that the method has higher accuracy ratios and lower false negative values under some post processing operation compared to other DCT based methods. Our method can also detect multiple copy move forgery.

Improved copy-move forgery detection based on the CLDs and colour moments

Copy move forgery is one of the most widely used tampering techniques to hide undesirable objects in a scene. Existing techniques to detect such tampering aim to improve the robustness of the methods. A robust method based on the colour moments and colour layout descriptors (CLD) is proposed in this paper. The method divides the image into overlapping blocks. First colour moments of the blocks are used to group similar blocks into clusters. Blocks from the tampered regions are similar and going to be mapped into the same cluster. Thus, search for copied and moved regions in clusters to detect forgery can be done by separate threads. CLD is used to extract block features, which makes the method more robust to post-processing operations. Experimental results indicate that the proposed method can detect copy move forgery even if additive white Gaussian noise, JPEG compression or Gaussian blurring is used after forgery.

Image forgery detection using colour moments

With the rapid development of powerful image editing software, digital image forgery has been a serious problem. One of the most commonly used forgery technique is the Copy-move forgery that copies a part of an image and pastes it on the other region in the same image. Some methods in the literature divide suspicious image into overlapped blocks and extract some features from them to judge the forgery. Similarity among the feature vectors gives a clue about the forgery. In this work, we used first three-color moments to extract feature vectors from the blocks. The method assumes that the color distribution of a block cannot be changed even if it is compressed or blurred. Color Moments has not been used to detect image forgery in the literature before. The proposed method has higher accuracy ratios compared to other works when the forged image is post processed using some operations.

Frame duplication detection based on BoW model

Duplicated sequence of frames in a video to cover up or replicate a scene is a video forgery. There are methods to authenticate video files, but embedding authentication information into videos requires extra hardware or software. It is possible to detect frame duplication forgery by carefully inspecting the content to discover high correlation among group of frames. A new frame duplication detection method based on Bag-of-Words (BoW) model is proposed in this paper. BoW is a model used in textual analysis first and image and video retrieval later by researchers. We used BoW to create visual words and build a dictionary from Scale Independent Feature Transform (SIFT) keypoints of frames in video. Frame features, i.e., visual word representations at keypoints, are used to detect sequence of duplicated parts in the video. The method computes thresholds depending on the content to improve both robustness and performance. The proposed method is tested on 31 test videos selected from Surrey University Library for Forensic Analysis (SULFA) and from various movies. Experimental results show a better detection performance and reduced run time compared to similar methods reported in the literature.

A fast detection method for frame duplication forgery based on correlation

In recent years, fast development of video editing software has made video forgery applicable. One of the most frequently encountered forgery types in video forensics is the frame duplication forgery. Researches have proposed methods to deal with this type of forgery. The two main drawbacks of this methods reported in the literature are execution time and low detection accuracy. In this work a new frame duplication forgery detection method that uses correlation between neighboring frames to extract features from video is proposed. Experimental results show that the proposed method has lower execution time with better detection accuracy compared to similar works reported in the literature.

Image forgery detection based on SIFT and k-means++

Copy move attack, a special type of image forgery, is performed by copying a part of the image and pasting anywhere else in the same image. Besides block-based methods, keypoint-based methods like Scale Invariant Feature Transform (SIFT) are improved for detection of copy move attacks. In this method, firstly image keypoints are extracted and a 128 dimensional feature vector named as SIFT descriptor is generated for each keypoint. Then, these keypoints are matched using Euclidean distance among their descriptors. Although this method is good at detection of copy move attacks, it has drawback. Computational complexity is huge and increases with the size of the image. To overcome this drawback, we propose to use k-means++ method for clustering the SIFT descriptors. Thus, each keypoint is matched with keypoints only in its cluster instead of all other keypoints. This proposed hybrid method allows us to decrease the time complexity of the SIFT method considerably.

A novel keypoint based forgery detection method based on local phase quantization and SIFT

Increase on the availability of the image editing software makes digital image forgery serious problem. Researchers proposed methods to cope with image authentication in recent years. We proposed a novel keypoint based passive image authentication technique to determine the copy move forgery. The method extracts the structural texture information from the test image by using LPQ (Local Phase Quantization) operator to make the keypoint extraction techniques more successful. SIFT is used extract the keypoints from texture image. Forged regions are detected by matching the keypoints. The method also improves the keypoint based passive image authentication mechanism by extraction texture information before keypoint extraction. Experimental results show that, the method detects forged regions on the images even if the forged image has undergone some attacks (Gaussian blurring/Additive White Gaussian Noise and jpeg compression).