Detecting Image Spam Based on File Properties, Histogram and Hough Transform

Win, Zin Mar; Aye, Nyein

doi:10.7763/jacn.2014.v2.127

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…However, the resulting performance and the detection accuracy depends on the type and number of image features used. Different author manually generate the image features based on properties and meta data of the image file [17], global features including color and gradient histogram of the image file [18][19][20][21][22][23][24], some form of low level image features [25][26][27][28][29], Image texture based features related to run-length matrix, auto-regressive model, co-occurrence matrix, wavelet transform, histogram and gradient [30][31][32]. Other work uses image features based on Speeded Up Robust Features (SURF) [33] and n-gram feature from the Base64 format of the image file [34].…”

Section: Image Spam Detectionmentioning

confidence: 99%

Application of error level analysis in image spam classification using deep learning model

Singh,

Singh

2023

PLoS ONE

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Image spam is a type of spam that contains text information inserted in an image file. Traditional classification systems based on feature engineering require manual extraction of certain quantitative and qualitative image features for classification. However, these systems are often not robust to adversarial attacks. In contrast, classification pipelines that use convolutional neural network (CNN) models automatically extract features from images. This approach has been shown to achieve high accuracies even on challenge datasets that are designed to defeat the purpose of classification. We propose a method for improving the performance of CNN models for image spam classification. Our method uses the concept of error level analysis (ELA) as a pre-processing step. ELA is a technique for detecting image tampering by analyzing the error levels of the image pixels. We show that ELA can be used to improve the accuracy of CNN models for image spam classification, even on challenge datasets. Our results demonstrate that the application of ELA as a pre-processing technique in our proposed model can significantly improve the results of the classification tasks on image spam datasets.

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Section: Image Spam Detectionmentioning

confidence: 99%

Application of error level analysis in image spam classification using deep learning model

Singh,

Singh

2023

PLoS ONE

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“…e second type of image spam classification approach uses various image features and uses various machine learning techniques in the classification process. Some of the works use image features that are based on file properties and metadata [10], global image features including color and gradient histograms [11][12][13][14][15][16][17], low-level image features [18][19][20][21][22], image texture-based features related to a histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet transform [23][24][25]. Other works use image features such as Speeded Up Robust Feature (SURF) [26] and n-gram after converting the image to a string of its Base64 format [27].…”

Section: Related Workmentioning

confidence: 99%

An Improved Image Spam Classification Model Based on Deep Learning Techniques

Singh

Chanu

et al. 2022

Security and Communication Networks

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Image Spam is a type of spam that has embedded text in an image. Classification of Image Spam is done using various machine learning approaches based on a broad set of features extracted from the image. For its remarkable results, the convolutional neural networks (CNN) are widely used in image classification as well as feature extraction tasks. In this research, we analyze image spam using a CNN model based on deep learning techniques. The proposed model is fine-tuned and optimized for both feature extraction as well as for classification tasks. We also compared our proposed model to different “Improved” and “Challenge” image spam datasets, which were developed for increasing the difficulty of the classification task. Our model significantly improves the accuracy of the classification task as compared to other approaches on the same datasets.

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“…• Metadata Domain: Compression ratio and Aspect ratio of the images are calculated as the two metadata features [39]. Compression ratio captures the amount of compression achieved by calculating the ratio of pixels in an image to an actual image size.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Previous approaches to detecting image spam consist of extracting various image properties and classifying the images as either spam or ham (i.e., non-spam), using machine learning techniques. This previous work either obtains low accuracy [9,12,39] or has an unrealistically high computational complexity [7,19].…”

Section: Table Of Contents Chapter Introductionmentioning

confidence: 99%

Image Spam Analysis

Annadatha

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Detecting Image Spam Based on File Properties, Histogram and Hough Transform

Cited by 11 publications

References 6 publications

Application of error level analysis in image spam classification using deep learning model

Application of error level analysis in image spam classification using deep learning model

An Improved Image Spam Classification Model Based on Deep Learning Techniques

Image Spam Analysis

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