Deep Neural Networks Based Error Level Analysis for Lossless Image Compression Based Forgery Detection

In the last years, there has been growing a large increase in digital imaging techniques, and their applications became more and more pivotal in many critical scenarios. Conversely, hand in hand with this technological boost, imaging forgeries have increased more and more along with their level of precision. In this view, the use of digital tools, aiming to verify the integrity of a certain image, is essential. Indeed, insurance is a field that extensively uses images for filling claim requests and a robust forgery detection is essential. This paper proposes an approach which aims to introduce a full-automated system for identifying potential splicing frauds in images of car plates by overcoming traditional problems using artificial neural networks (ANN). For instance, classic fraud-detection algorithms are impossible to fully automatize whereas modern deep learning approaches require vast training datasets that are not available most of the time. The method developed in this paper uses Error Level Analysis (ELA) performed on car license plates as an input for a trained model which is able to classify license plates in either original or forged.

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Lossless Image Compression by Joint Prediction of Pixel and Context Using Duplex Neural Networks

IEEE Access ◽

10.1109/access.2021.3088936 ◽

2021 ◽

pp. 1-1

Author(s):

Hochang Rhee ◽

Yeong Il Jang ◽

Seyun Kim ◽

Nam Ik Cho

Keyword(s):

Neural Networks ◽

Image Compression ◽

Lossless Image Compression ◽

Joint Prediction

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A Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis

Entropy ◽

10.3390/e22020249 ◽

2020 ◽

Vol 22 (2) ◽

pp. 249

Author(s):

Weiguo Zhang ◽

Chenggang Zhao ◽

Yuxing Li

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Image Compression ◽

Direct Detection ◽

Detection Methods ◽

Extraction Technique ◽

Error Level ◽

Face Area ◽

Level Analysis ◽

Feature Extraction Technique

The quality and efficiency of generating face-swap images have been markedly strengthened by deep learning. For instance, the face-swap manipulations by DeepFake are so real that it is tricky to distinguish authenticity through automatic or manual detection. To augment the efficiency of distinguishing face-swap images generated by DeepFake from real facial ones, a novel counterfeit feature extraction technique was developed based on deep learning and error level analysis (ELA). It is related to entropy and information theory such as cross-entropy loss function in the final softmax layer. The DeepFake algorithm is only able to generate limited resolutions. Therefore, this algorithm results in two different image compression ratios between the fake face area as the foreground and the original area as the background, which would leave distinctive counterfeit traces. Through the ELA method, we can detect whether there are different image compression ratios. Convolution neural network (CNN), one of the representative technologies of deep learning, can extract the counterfeit feature and detect whether images are fake. Experiments show that the training efficiency of the CNN model can be significantly improved by the ELA method. In addition, the proposed technique can accurately extract the counterfeit feature, and therefore achieves outperformance in simplicity and efficiency compared with direct detection methods. Specifically, without loss of accuracy, the amount of computation can be significantly reduced (where the required floating-point computing power is reduced by more than 90%).

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Exposing Face-Swap Images Based on Deep Learning and ELA Detection

Proceedings ◽

10.3390/ecea-5-06684 ◽

2019 ◽

Vol 46 (1) ◽

pp. 29

Author(s):

Weiguo Zhang ◽

Chenggang Zhao

Keyword(s):

Information Theory ◽

Deep Learning ◽

Image Compression ◽

Detection Accuracy ◽

Error Level ◽

Training Time ◽

Normalized Mutual Information ◽

Face Area ◽

Facial Images ◽

Level Analysis

New developments in artificial intelligence (AI) have significantly improved the quality and efficiency in generating fake face images; for example, the face manipulations by DeepFake are so realistic that it is difficult to distinguish their authenticity—either automatically or by humans. In order to enhance the efficiency of distinguishing facial images generated by AI from real facial images, a novel model has been developed based on deep learning and error level analysis (ELA) detection, which is related to entropy and information theory, such as cross-entropy loss function in the final Softmax layer, normalized mutual information in image preprocessing, and some applications of an encoder based on information theory. Due to the limitations of computing resources and production time, the DeepFake algorithm can only generate limited resolutions, resulting in two different image compression ratios between the fake face area as the foreground and the original area as the background, which leaves distinctive artifacts. By using the error level analysis detection method, we can detect the presence or absence of different image compression ratios and then use Convolution neural network (CNN) to detect whether the image is fake. Experiments show that the training efficiency of the CNN model can be significantly improved by using the ELA method. And the detection accuracy rate can reach more than 97% based on CNN architecture of this method. Compared to the state-of-the-art models, the proposed model has the advantages such as fewer layers, shorter training time, and higher efficiency.

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Lossless image compression and reconstruction by cellular neural networks

Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2000) (Cat. No.00TH8509) ◽

10.1109/cnna.2000.876820 ◽

2002 ◽

Cited By ~ 4

Author(s):

M. Tanaka ◽

Y. Tanji ◽

M. Onishi ◽

T. Nakaguchi

Keyword(s):

Neural Networks ◽

Image Compression ◽

Cellular Neural Networks ◽

Lossless Image Compression

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Image Compression Based on Deep Learning: A Review

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v8i130193 ◽

2021 ◽

pp. 62-76

Author(s):

Hajar Maseeh Yasin ◽

Adnan Mohsin Abdulazeez

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Image Compression ◽

Recurrent Neural Networks ◽

Deep Neural Networks ◽

Review Paper ◽

High Accuracy ◽

Machine Learning Methods ◽

Digital Era ◽

Different Types

Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.

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