Forensic Detection Using Bit-Planes Slicing of Median Filtering Image

Rhee, Kang Hyeon

doi:10.1109/access.2019.2927540

Cited by 19 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the Cut and Paste region, one of them uses median filtering (MF) that has the property of preserving edges within an image [4,5]. For this reason, MF is frequently used in the operation of forgery images; thus, median filtering detection (MFD) is a necessity in image forensics [6]. In the typical image forensics detection (IFD), the relationship between image pixels is statistically processed, or the frequency and spatial domains of an image are analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…feature vector with the three characteristics of the feature set: 'the Canny edge' [10], 'the Prewitt gradient' [11], and the 'Hu invariant moments' [12] for MFD too. In the image forensics detection (IFD), the median filter residual (MFR) is mainly used as a preprocessing step [5][6][7]9,12,13] of the suspicion image. For median filtering forensics, J. Chen et al [5] proposed the framework based on CNN (Convolutional Neural Network), in which the first layer is a filter layer as the MFR.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual

Rhee

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

In the image forensics, detection of Cut-Paste manipulation is complicated computing. In this paper, the texture analysis of the spliced image is used to detect image forensics. From the local entropy of the median filter residual (MFR) of the forged image, the feature set is extracted for the ground truth mask 'Find Gray level regional Maxima (FGM),' and 'Entropy-based Edge (EbE).' Also, from the local range, the feature set is extracted for ground truth mask {'Morphological-Open Image (MOI), and 'Morphological-Erosion Image (MOE)'}. The feature vector in this paper composed of the two MOIs, two MOEs, and one EbE. The defined novel feature vector trained on a cubic support vector machine (SVM) classifier for only the performance evaluation of the proposed scheme. The performance of the proposed image forensics detection (IFD) scheme was measured with the five transformed types of image: median filtered (window size: {3×3, 5×5}), JPEG compressed (quality factor: {90, 70}) and average filtered (window size: 3×3). For the detection of the spliced image forensics, the region of Cut-Paste is classified by an input image only to the proposed scheme without the need for the trained SVM classifier. Throughout the experiment, the accuracy of median filtering detection was 98% over. Also, The area under the curve by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed IFD scheme approached to '1' with the trained cubic SVM classifier. Experimental results show high efficiency and performance to the spliced image. Therefore, the grade evaluation of the proposed scheme is "Excellent (A)." INDEX TERMS Forgery image, Median filtering detection, Spliced image forensics, Median filter residual, Texture analysis, Support vector machine.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual

Rhee

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…These section further upgrades more research work towards a similar topic. Most recently, the adoption of a slicing-based approach towards the bit planes has been carried out by Rhee [11]. The authors have also used the method for the identification of median filtering.…”

Section: Introductionmentioning

confidence: 99%

Novel framework for optimized digital forensic for mitigating complex image attacks

Shashidhar¹,

Ramesh²

2020

IJECE

View full text Add to dashboard Cite

Digital Image Forensic is significantly becoming popular owing to the increasing usage of the images as a media of information propagation. However, owing to the presence of various image editing tools and softwares, there is also an increasing threats over image content security. Reviewing the existing approaches of identify the traces or artifacts states that there is a large scope of optimization to be implmentation to further enhance teh processing. Therfore, this paper presents a novel framework that performs cost effective optmization of digital forensic tehnqiue with an idea of accurately localizing teh area of tampering as well as offers a capability to mitigate the attacks of various form. The study outcome shows that propsoed system offers better outcome in contrast to existing system to a significant scale to prove that minor novelty in design attribute could induce better improvement with respect to accuracy as well as resilience toward all potential image threats.

show abstract

“…There are three forgery methods to manipulate images by malicious ones: copymove, cut -paste, and double compression, etc. [4][5][6]. A new composite image with several different images is quickly made by forgers who prefer the Cut-Paste.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, MF is often used in the manipulation of forgery images. So, research of the median filtering detection (MFD) is essential in image forensics [6]. In the typical image forensics detection (IFD), the relationship between image pixels is statistically processed, or the frequency and spatial domains of an image are analyzed.…”

Section: Introductionmentioning

confidence: 99%

Composition of Visual Feature Vector Pattern for Deep Learning in Image Forensics

Rhee

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

In image forensics, to determine whether the image is impurely transformed, it extracts and examines the features included in the suspicious image. In general, the features extracted for the detection of forgery images are based on numerical values, so it is somewhat unreasonable to use in the CNN structure for image classification. In this paper, the extraction method of a feature vector is using a least-squares solution. Treat a suspicious image like a matrix and its solution to be coefficients as the feature vector. Get two solutions from two images of the original and its median filter residual (MFR). Subsequently, the two features were formed into a visualized pattern and then fed into CNN deep learning to classify the various transformed images. A new structure of the CNN net layer was also designed by hybrid with the inception module and the residual block to classify visualized feature vector patterns. The performance of the proposed image forensics detection (IFD) scheme was measured with the seven transformed types of image: average filtered (window size: 3×3), gaussian filtered (window size: 3×3), JPEG compressed (quality factor: {90, 70}), median filtered (window size: {3×3, 5×5}), and unaltered. The visualized patterns are fed into the image input layer of the designed CNN hybrid model. Throughout the experiment, the accuracy of median filtering detection was 98% over. Also, the area under the curve (AUC) by sensitivity (TP: true positive rate) and 1specificity (FP: false positive rate) results of the proposed IFD scheme approached to '1' on the designed CNN hybrid model. Experimental results show high efficiency and performance to classify the various transformed images. Therefore, the grade evaluation of the proposed scheme is "Excellent (A)."

show abstract

Forensic Detection Using Bit-Planes Slicing of Median Filtering Image

Cited by 19 publications

References 9 publications

Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual

Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual

Novel framework for optimized digital forensic for mitigating complex image attacks

Composition of Visual Feature Vector Pattern for Deep Learning in Image Forensics

Contact Info

Product

Resources

About