Performance Analysis of Image BOUNDARY PRESERVING MASK R-CNN
Implementation plan:
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Scenario 1:(Using Casia-V2 Dataset)
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Step 1: Initially, we collect and load Casia V2 Dataset.
Step 2: Then, we pre-process the collected data from Dataset.
Step 3: Next, segmentation is performed based on pixel-level predictions.
Step 4: Apply Boundary-preserving Mask R-CNN and train model.
Step 6: Predict forged region
step 7: Finally, we plot performance for the following metrics:
6.1: Number of epochs vs. accuracy (%)
6.2: Number of epochs vs. Recall (%)
6.3: Number of epochs vs. Precision (%)
6.4: Number of epochs vs. F1-score (%)
6.5. Training and validation loss(graph)
6.5: AUC ROC Curve (Graph separately)
6.6 IOU(Intersection over Union)-(%)
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Scenario 2:(Using Columbia Dataset)
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Step 1: Initially, we collect and load Columbia Dataset.
Step 2: Then, we pre-process the collected data from Dataset.
Step 3: Next, segmentation is performed based on pixel-level predictions.
Step 4: Apply Boundary-preserving Mask R-CNN and train model
Step 6: Predict forged region
step 7: Finally, we plot performance for the following metrics:
6.1: Number of epochs vs. accuracy (%)
6.2: Number of epochs vs. Recall (%)
6.3: Number of epochs vs. Precision (%)
6.4: Number of epochs vs. F1-score (%)
6.5. Training and validation loss(graph)
6.5: AUC ROC Curve (Graph separately)
6.6 IOU(Intersection over Union)-(%)
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Scenario 3:(Using Carvalho Dataset)
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Step 1: Initially, we collect and load Carvalho Dataset.
Step 2: Then, we pre-process the collected data from Dataset.
Step 3: Next, segmentation is performed based on pixel-level predictions.
Step 4: Apply Boundary-preserving Mask R-CNN and train model
Step 6: Predict forged region
step 7: Finally, we plot performance for the following metrics:
6.1: Number of epochs vs. accuracy (%)
6.2: Number of epochs vs. Recall (%)
6.3: Number of epochs vs. Precision (%)
6.4: Number of epochs vs. F1-score (%)
6.5. Training and validation loss(graph)
6.5: AUC ROC Curve (Graph separately)
6.6 IOU(Intersection over Union)-(%)
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Scenario 4:(Using comofod Dataset)
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Step 1: Initially, we collect and load comofod Dataset.
Step 2: Then, we pre-process the collected data from Dataset.
Step 3: Next, segmentation is performed based on pixel-level predictions.
Step 4: Apply Boundary-preserving Mask R-CNN and train model
Step 6: Predict forged region
step 7: Finally, we plot performance for the following metrics:
6.1: Number of epochs vs. accuracy (%)
6.2: Number of epochs vs. Recall (%)
6.3: Number of epochs vs. Precision (%)
6.4: Number of epochs vs. F1-score (%)
6.5. Training and validation loss(graph)
6.5: AUC ROC Curve (Graph separately)
6.6 IOU(Intersection over Union)-(%)
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Scenario 5:(Using MICC F220 Dataset)
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Step 1: Initially, we collect and load MICC F220 Dataset.
Step 2: Then, we pre-process the collected data from Dataset.
Step 3: Next, segmentation is performed based on pixel-level predictions.
Step 4: Apply Boundary-preserving Mask R-CNN and train model.
Step 6: Predict forged region
step 7: Finally, we plot performance for the following metrics:
6.1: Number of epochs vs. accuracy (%)
6.2: Number of epochs vs. Recall (%)
6.3: Number of epochs vs. Precision (%)
6.4: Number of epochs vs. F1-score (%)
6.5. Training and validation loss(graph)
6.5: AUC ROC Curve (Graph separately)
6.6 IOU (Intersection over Union)- (%)
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Scenario 6:(Using CG-1050Dataset):
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Step 1: Initially, we collect and load CG-1050 Dataset.
Step 2: Then, we pre-process the collected data from Dataset.
Step 3: Next, segmentation is performed based on pixel-level predictions
Step 4: Apply Boundary-preserving Mask R-CNN and train model
Step 6: Predict forged region
step 7: Finally, we plot performance for the following metrics:
6.1: Number of epochs vs. accuracy (%)
6.2: Number of epochs vs. Recall (%)
6.3: Number of epochs vs. Precision (%)
6.4: Number of epochs vs. F1-score (%)
6.5. Training and validation loss(graph)
6.5: AUC ROC Curve (Graph separately)
6.6 IOU (Intersection over Union)- (%)
Dataset Link:
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1. Casia V2 Dataset: https://www.kaggle.com/datasets/divg07/casia-20-image-tampering-detection-dataset/data (Rename Folder Names to Dataset Name)
2. Columbia Dataset: https://www.dropbox.com/sh/786qv3yhvc7s9ki/AACbEEzGPrD3_y38bpWHzgdqa?dl=0 (Rename Folder Names to Dataset Name)
3. Carvalho Dataset:http://ic.unicamp.br/~rocha/pub/downloads/2014-tiago-carvalho-thesis/tifs-database.zip (Rename Folder Names to Dataset Name)
4. CoMoFoD: https://www.vcl.fer.hr/comofod/comofod_small.rar (Rename Folder Names to Dataset Name)
5. MICC F220: We have attached Dataset in Respective Dataset Folder
6. CG-1050: https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/dk84bmnyw9-2.zip (Rename Folder Names to Dataset Name)
Software Requirement:
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1. Development Tool: Python – 3.11.4 or above
2. Operating System: Windows 10 (64-bit)
Note:
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the above plan satisfies your requirement please confirm with us.
4) We develop simulation based projects only, not in real time
