Semi fragile watermarking using Gaussian mixture model for malicious image attacks

Gopalakrishnan, T.; Ramakrishnan, S.; Balasamy, K.; Murugavel, A.S. Muthananda

doi:10.1109/wict.2011.6141229

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…Table 3 shows the results of false alarm rate and missing alarm rate. Form the results of table 3, the performance of our method is better than that of [ 9] and close to that of [3]. However, the watermarking of [3] is unconcerned with the image, so the original watermarking is required when authenticating.…”

Section: Experiments Of Comparing With Other Methodsmentioning

confidence: 82%

See 1 more Smart Citation

Adaptive semi-fragile watermarking based on complete quantization and image contents

Guo¹,

Wei²,

Tang³

et al. 2013

Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation

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Complete quantization principle is proposed in this paper, based on which, an adaptive semi-fragile watermarking algorithm based on image contents is proposed. The algorithm uses the low frequency sub-band to construct watermarking related to the content of image, and then uses just noticeable difference of wavelet domain to embed watermarking by means of complete quantitative principle. Experiments show this algorithm can classify malicious and non-malicious manipulations accurately.

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Section: Experiments Of Comparing With Other Methodsmentioning

confidence: 82%

“…We compare our method with the methods in [3] and [9], which perform better than most of semi-fragile watermarking algorithm. Table 3 shows the results of false alarm rate and missing alarm rate.…”

Section: Experiments Of Comparing With Other Methodsmentioning

confidence: 99%

Adaptive semi-fragile watermarking based on complete quantization and image contents

Guo¹,

Wei²,

Tang³

et al. 2013

Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation

View full text Add to dashboard Cite

show abstract

Section: B Experiments Of Malicious Tampermentioning

confidence: 82%

“…C. Experiment of comparing with other methods We compare our method with the methods in [3] and [9], which perform better than most of semi-fragile watermarking algorithm. Table 3 shows the results of false alarm rate and missing alarm rate.…”

Section: B Experiments Of Malicious Tampermentioning

confidence: 99%

Adaptive Semi-Fragile Watermarking Based on Complete Quantization and Image Contents

Wei

Tang

et al. 2013

AMM

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“…The ultimate result of a typical CNN network structure is often one or more fully-connected layers [17,18,19], however the network itself is typically composed of convolutional and pooling layers. The final output of the layer is obtained by applying a bias to fully connected layer and activation function.…”

Section: Proposed Modelmentioning

confidence: 99%

Deep Learning and Classification Algorithms for COVID-19 Detection

Sidheeque¹,

Sumathy²,

M³

2022

IJACSA

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The imaging modalities of chest X-rays and computed tomography (CT) are commonly utilized to quickly and accurately diagnose COVID-19. Due to time and human error, it is exceedingly difficult to manually identify the infection using radio imaging. COVID-19 identification is being mechanized and improved with the use of artificial intelligence (AI) tools that have already showed promise. This study employs the following methodology: The chest footage was pre-processed by setting equalizing the histogram, sharpening it, and so on. The transformed chest images are then retrieved through shallow and high-level feature mapping over the backbone network. To further improve the classification performance of the convolutional neural network, the model uses self-attained mechanism through feature maps. Numerous simulations show that CT image classification and augmentation may be accomplished with higher efficiency and flexibility using the Inception-Resnet convolutional neural network than with traditional segmentation methods. The experiment illustrates the association between model accuracy, model loss, and epoch. Inception-statistical Resnet's measurement results are 98%, 91%, 91%.

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Semi fragile watermarking using Gaussian mixture model for malicious image attacks

Cited by 10 publications

References 12 publications

Adaptive semi-fragile watermarking based on complete quantization and image contents

Adaptive semi-fragile watermarking based on complete quantization and image contents

Adaptive Semi-Fragile Watermarking Based on Complete Quantization and Image Contents

Deep Learning and Classification Algorithms for COVID-19 Detection

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