Real-time detecting one specific tampering operation in multiple operator chains

Gao, Shangde; Liao, Xin; Liu, Xuchong

doi:10.1007/s11554-019-00860-3

Cited by 17 publications

(3 citation statements)

References 27 publications

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“…Additionally, the interpolation kernel, antialiasing filter also effect the detection performance. In [26] an information theoretical framework is proposed to quantify the detectability of one operation in operation chain. In actual operation chain detection, the parameters of operations and the proportion of training and testing image set are tuned to find the best SVM-based detector.…”

Section: Related Workmentioning

confidence: 99%

“…In the case of operation chain, the uncertainty of operation feature is altered under the attack due to the following operation. In [23], [24], [26] the mutual information has been introduced to evaluate the capability of the specific forensics feature. Motivated by these works, we use the mutual information to measure the uncertainty variation of feature in the case of operation chain.…”

Section: B Mutual Information Scalementioning

confidence: 99%

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The Forensicability of Operation Detection in Image Operation Chain

2022

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A large number of forensics research focus on operation detection to reveal the evidence of forgery action in the digital image. In the early works, analyst firstly model the probability distribution of single operation, and design the forensic tools based on feature extraction and machine learning based classifier. With increasing dimension of the feature and facing multiple operations detection scenario, the physical meaning of the feature gradually become ambiguous. Especially, since deep learning algorithm was used in forensic research, the automatic feature selection and making decision with high performance of classification conceals the intrinsic forensic clues. In this paper, we explore the availability of feature for operation detection in the operation chain, so called forensicability. An anti-forensic attack algorithm is introduced to formulate the impact on the feature due to the following operation. We propose two measurements: attack angle and scale, mutual information scale to indicate the forensic feature variation after the image manipulated by the following operation. The uncoupled relationship can be revealed by our methods. In the experiments, four operation chains involving ten operations are considered as the case study. The results are encouraging and improve the explanation of the forensics method based on high dimensional features.INDEX TERMS Forensicability, Image operation chain detection, Image forensics.

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Section: Related Workmentioning

confidence: 99%

Section: B Mutual Information Scalementioning

confidence: 99%

The Forensicability of Operation Detection in Image Operation Chain

2022

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“…But, the detection performance was not improved. An information theoretical framework was developed in [5] to quantify the detectability of one particular operation in multiple chains. However, the processing time was not reduced.…”

Section: Literature Surveymentioning

confidence: 99%

Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

Arun

Kumar

2021

Multimed Tools Appl

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Occlusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional techniques do not recognize traffic signs well due to the vehicles are occluded. Besides occlusion removal was not performed in existing techniques with a less amount of time. In order to overcome such limitations, Non-linear Gaussian Bilateral Filtered Sorenson–Dice Exemplar Image Inpainting Based Bayes Conditional Probability (NGBFSEII-BCP) Method is proposed. Initially, a number of remote sensing images are taken as input from Highway Traffic Dataset. Then, the NGBFSEII-BCP method applies the Non-Linear Gaussian Bilateral Filtering (NGBF) algorithm for removing the noise pixels in input images. After preprocessing, the NGBFSEII-BCP method is used to remove the occlusion in the input images. Finally, NGBFSEII-BCP Method applies Bayes conditional probability to find operation status and thereby gets higher road safety using remote sensing images. The technique conducts the simulation evaluation using metrics such as peak signal to noise ratio, computational time, and detection accuracy. The simulation result illustrates that the NGBFSEII-BCP Method increases the detection accuracy by 20% and reduces the computation time by 32% as compared to state-of-the-art works.

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