Detection and localization of image forgeries using improved mask regional convolutional neural network

Wang, Xin‐Yi; Wang, He; Niu, Shao Zhang; Zhang, Ji Wei

doi:10.3934/mbe.2019229

Cited by 61 publications

(32 citation statements)

References 26 publications

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“…The decoder model gives the finer representation of the spatial map, which offers the altered region in an image. An improved mask R-CNN model [15] (regional convolutional neural network) is proposed with a Sobel filter to recognize the altered and unaltered region's distinctive features. This network handles two types of forgeries, such as copy move and splicing.…”

Section: Forgery Type Independentmentioning

confidence: 99%

AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Kadam¹,

Ahirrao²,

Kotecha³

2021

IJECE

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Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.

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Section: Forgery Type Independentmentioning

confidence: 99%

AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Kadam¹,

Ahirrao²,

Kotecha³

2021

IJECE

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“…Second, these techniques are mostly tuned to accomplish great performance on specific dataset(s) yet fail in other datasets [86]. Third, handcrafted features are usually have restricted discrimination power [89], [90].…”

Section: Deep Learning Based Cmfd Techniquesmentioning

confidence: 99%

Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Zedan¹,

Soliman²,

Elsayed³

et al. 2021

IJACSA

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Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image. Copy-move forgery detection (CMFD) has currently become an active research topic in the passive/blind image forensics field. There has no doubt that conventional techniques and especially the keypoint based techniques have pushed the CMFD forward in the previous two decades. However, CMFD techniques in general and conventional techniques in particular suffer from several challenges. And thus, increasing approaches are exploiting deep learning for CMFD. In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective.We classify the CMFD techniques into several classifications according to the detection methodology, the detection paradigm, and the detection capability . We discuss the challenges facing the CMFD techniques as well as the ways for solving them. In addition, this survey covers the evaluation metrics and datasets commonly utilized for CMFD. Also, we are debating and proposing certain plans for future research. This survey will be helpful for the researchers' as it master the recent trends of CMFD and outline some future research directions.

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“…Works related to this technique allow the recognition of different objects, for example, a model capable of detecting two types of image manipulations: copy-move and splicing, in order to perform the detection, location and segmentation of the fake images in forensic analysis, where an average accuracy of 97.8 % is achieved [13]. Also, an automated detector of strawberry based on Mask R-CNN in order to improve the manual harvest in the strawberry industry, with an average accuracy rate of 95.78 % and a recall of 95.41 % [36].…”

Section: B Mask R-cnnmentioning

confidence: 99%

“…However, nowadays, there are several machine learning techniques for the processing of images that have achieved high precision for a number of tasks, such as the classification of images, the detection and locating of objects, one of them is Mask R-CNN, which has achieved precisions of 97.8 %, 95.78 %, 98.5 %, and 85 % by solving problems of detection of fake images [13], detection of skin burns regions [14], detection of workers and danger zones in a building [15], and detection of breast lesions [16], respectively. So, Mask R-CNN could be used to solve DBIS.…”

Section: Introductionmentioning

confidence: 99%

Bullet Impact Detection in Silhouettes Using Mask R-CNN

Vilchez

Mauricio

2020

IEEE Access

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The bullet impact detection in the silhouette plays an important role in the institutions which have a shooting range, specially where are regulated the license to carry a gun and where the evaluation manual process could present distortion in the ability's evaluation of the shooter and like a consequence the license's issuance. This paper proposes a method for an automatic detection of the bullet impacts in silhouettes based on deep learning and image processing, which consist of the following steps: preprocessing, impacts detection, edge detection and evaluation results. The experiments about 600 silhouettes with 2401 bullet impacts images of the proposed and implemented method considering the models Resnet 50 and Resnet 101 for Mask R-CNN show that Resnet 50 get better results than Resnet 101, achieving 97.6 %, 99.5 %, and 97.9 %, of accuracy, precision and recall, respectively, above the methods Circular Hough Transform, Circlet Detection, Random Sample Consensus, Randomized Hough Transform, Randomized Circle Detection, Support Vector Machine, Faster R-CNN, MnasNet and YOLO. Also, the results show 100 % of effectiveness in the edge detection and the count of the detected bullet impacts.INDEX TERMS Edge detection, bullet impact detection, image processing, Mask R-CNN.

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Detection and localization of image forgeries using improved mask regional convolutional neural network

Cited by 61 publications

References 26 publications

AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Bullet Impact Detection in Silhouettes Using Mask R-CNN

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