Image Resampling Detection Based on Convolutional Neural Network

Liang, Yaohua; Fang, Yanmei; Luo, Shangjun; Chen, Bing

doi:10.1109/cis.2019.00061

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…Thus, takes a lot of time and CNN fails to bring a good correlation among these patches. Similarly, if we traverse through horizontal direction over the rows will result in the same problem [19], [20]. Thus, in this work for establishing a good correlation among both directions here, we introduce an additional layer namely the correlation layer.…”

Section: Preprocessing and Resampling Feature Detection And Extractionmentioning

confidence: 99%

“…Segmentation of tampered regions is a challenging task. Recently, CNN-based semantic segmentation methodologies [20], [21] have attained wide attention. In [21], used fully connected CNN for analyzing region shape and object content by extracting feature sets at different levels in a hierarchical manner.…”

Section: Introductionmentioning

confidence: 99%

“…In [21], used fully connected CNN for analyzing region shape and object content by extracting feature sets at different levels in a hierarchical manner. The CNN-based framework works very well in the area of object detection [19] and segmentation [20], [21] in learning and a better understanding of the content of different segments. Unlike object segmentation, tampered segments could be copied objects from different regions of an image or could be removed objects.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, tampering region segmentation using a standard convolution neural network may not produce a good result. In [4] carried out a comparative analysis of various existing tampering region segmentation methodologies [20], [21] and showed they do not perform well for object removal and copy-move tampering [22]- [25]. Image forgeries create certain artifacts such as compression, and resampling, which can be better learned using resampling features [6], [26].…”

Section: Introductionmentioning

confidence: 99%

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Extraction of image resampling using correlation aware convolution neural networks for image tampering detection

Manjunatha.¹,

Patil

2022

IJECE

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<span>Detecting hybrid tampering attacks in an image is extremely difficult; especially when copy-clone tampered segments exhibit identical illumination and contrast level about genuine objects. The existing method fails to detect tampering when the image undergoes hybrid transformation such as scaling, rotation, compression, and also fails to detect under small-smooth tampering. The existing resampling feature extraction using the Deep learning techniques fails to obtain a good correlation among neighboring pixels in both horizontal and vertical directions. This work presents correlation aware convolution neural network (CA-CNN) for extracting resampling features for detecting hybrid tampering attacks. Here the image is resized for detecting tampering under a small-smooth region. The CA-CNN is composed of a three-layer horizontal, vertical, and correlated layer. The correlated layer is used for obtaining correlated resampling feature among horizontal sequence and vertical sequence. Then feature is aggregated and the descriptor is built. An experiment is conducted to evaluate the performance of the CA-CNN model over existing tampering detection methodologies considering the various datasets. From the result achieved it can be seen the CA-CNN is efficient considering various distortions and post-processing attacks such joint photographic expert group (JPEG) compression, and scaling. This model achieves much better accuracies, recall, precision, false positive rate (FPR), and F-measure compared existing methodologies.</span>

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Section: Preprocessing and Resampling Feature Detection And Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extraction of image resampling using correlation aware convolution neural networks for image tampering detection

Manjunatha.¹,

Patil

2022

IJECE

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“…The study shows that the existing model performs badly in detecting copy-clone and objectremoval. Using the resampling feature [4] the artifacts were created (i.e., resampling, compression) using tampered images can be learned [15]. The resampling attack generally occasionally allows correlation among pixels because of interpolation.…”

Section: Introductionmentioning

confidence: 99%

Tampering Detection and Segmentation Model for Multimedia Forensic

Patil,

et al. 2023

IJACSA

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When an image undergoes hybrid post-processing transformation, detecting tamper region, localizing it and segmentation becomes very difficult tasks. In particular, when a copy-move attack with hybrid transformation has similar contrast and illumination parameters with an authenticated image it makes tamper detection difficult. Alongside, under small-smooth attack existing tamper identification model provides a very poor segmentation outcome and sometimes fails to identify an image as tampered. This article focused on addressing the difficulty through the adoption of the Deep Learning model. The proposed technique is efficient in detecting tampering with good segmentation outcomes. However, existing models fail to distinguish adjacent pixels' relationships affecting segmentation outcomes. In this paper, an Improved Convolution Neural Network (ICNN) assuring correlation awareness-based Tamper Detection and Segmentation (TDS) model for image forensics is presented. This model brings good correlation among adjacent pixels through the introduction of an additional layer namely the correlation layer alongside vertical and horizontal layers. The TDS-ICNN is very effective in localizing and segmenting tamper regions even under small-smooth postprocessing tampering attacks by using a feature descriptor built using aggregated three-layer ICNN architecture. An experiment is done to study TDS-ICNN with other tamper identification models using various datasets such as MICC, Coverage, and CoMoFoD. The TDS-ICNN is very efficient under different postprocessing hybrid attacks when compared with existing models.

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