A Modern Approach for Image Forgery Detection using BRICH Clustering based on Normalised Mean and Standard Deviation

Nirmala, G.; Thyagharajan, K. K.

doi:10.1109/iccsp.2019.8697951

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…In addition, the complexity of creating and preserving flow for feature extraction is high. The BIRCH algorithm [52] fulfills data aggregation through a single-pass scan of the dataset and minimizes the input and output. We implement the feature extraction of multiple flow tables based on the BIRCH algorithm, which includes the definition of the cluster feature (CF) vector and the construction of a CF tree.…”

Section: B a Processing Methods Of Multiple Flow Tables For Sm-uanmentioning

confidence: 99%

SM-UAN: A Software-Defined Underwater Acoustic Network of Multi-Controllers for Inland Waterway Systems

et al. 2020

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Underwater acoustic network (UAN) is a lightweight system used in inland waterway systems. Software-defined networking (SDN) is a promising approach to improve the supervision abilities and integration services. This paper introduces SM-UAN, an SDN-based UAN with multi-controllers. Firstly, a hierarchical framework of SM-UAN is offered. Secondly, a multi-flow table structure and a multi-flow processing method is built. Followed by, a distributed deployment model of controllers and a load balancing mechanism are established. Finally, we construct an experimental scenario on Mininet with WOSS-NS3. The simulation demonstrates that the compression ratio is promoted to 24.1%, the compression ratio 29.4%, the packet forwarding rate 14.7% and the processing delay 31.2%, when comparing SM-UAN with the item-by-item matching (IM) system. For the load balancing mechanism, it shows that the maximum load for a master controller is balanced to 0.55, while the minimum load for a slave controller 0.46, which proves that a fair load distribution achieves. In addition, SM-UAN is compared with the traditional single-sink-based UAN (TS-UAN), the traditional multi-sink-based UAN (TM-UAN), and the SDN-based UAN with a single controller (SS-UAN). The results reveal that the survival time of SM-UAN is extended to 32.7%, 24.9%, and 21.6%, respectively. Also, the bit error rate (BER) of that is less than 10-4. In conclusion, advantages of SM-UAN have been highlighted, which provides theoretical support for inland waterway systems.

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Section: B a Processing Methods Of Multiple Flow Tables For Sm-uanmentioning

confidence: 99%

SM-UAN: A Software-Defined Underwater Acoustic Network of Multi-Controllers for Inland Waterway Systems

et al. 2020

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“…The S.D is another a statistic metric that expresses the degree of variation or dispersion among values of the group. A low S.D refers that the values of the set are typically close to the mean, whereas high S.D refer to values that are spread out over a larger range [26].…”

Section: B Abc Algorithmmentioning

confidence: 99%

Color Quantization Based Artificial Bee Colony and Training Tools

Reda¹,

Ibrahim²

2022

IJCDS

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Color quantization addressed as a useful method to help with limited disk capacity, network bandwidth, monitors true resolution. It works in reducing the true colors to the colors that the human eyes can detect and don't change the scene. In this paper a new method for color image quantization is proposed that depends on hybrid artificial tools. It consists of three main steps, the first is creating K colors to depend on them in quantizing the required colors in this step k-means algorithm is used starting from N colors. Then Artificial Bee Colony algorithm is used to improve the center of each cluster (color) which is combined to k-means algorithm as part of its optimization. Finally, the proposed color palette is used to quantize the image depending on the result of hybrid approach and apply to k-means. Two metrics are used to optimized the method (MSE, PSNR), both of them show good results for the quality of quantized images, as well as good human perception and good running time.

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“…Texture-based techniques are not resistant to attacks involving geometric transformations. Moment invariant, which includes blur moment, hue moment, Zernike moment, and other characteristics, is a set of features that are invariant to a geometric transformation in moment invariant-based approaches [10]. Each circular colour block that is overlapping is extracted for its QEM (quaternion exponent moment) moduli.…”

Section: Introductionmentioning

confidence: 99%

Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries

Singh

2023

Preprint

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With advancement in technology, especially in imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many forgery detection techniques have been developed from time to time. Copy Move Forgery is one such technique which is applied in conjunction with various other techniques to find forged regions within an image. For rapid and accurate detection of forged image, a novel hybrid technique is used in this research work that implements Gray Level Co-Occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoints (BRISK). GLCM significantly extracts key attributes from an image efficiently which will help to increase the detection accuracy. BRISK is known to be one of the 3 fastest modes of detection which will increase the execution speed of GLCM. BRISK even processes scaled and rotated images. Then the Principal Component Analysis (PCA) algorithm is applied in the final phase of detection will remove any unrequited element from the scene and highlights the concerned forged area.

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A Modern Approach for Image Forgery Detection using BRICH Clustering based on Normalised Mean and Standard Deviation

Cited by 11 publications

References 7 publications

SM-UAN: A Software-Defined Underwater Acoustic Network of Multi-Controllers for Inland Waterway Systems

SM-UAN: A Software-Defined Underwater Acoustic Network of Multi-Controllers for Inland Waterway Systems

Color Quantization Based Artificial Bee Colony and Training Tools

Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries

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