Convolutional Neural Networks for Image Spam Detection
- URL: http://arxiv.org/abs/2204.01710v1
- Date: Sat, 2 Apr 2022 15:10:44 GMT
- Title: Convolutional Neural Networks for Image Spam Detection
- Authors: Tazmina Sharmin and Fabio Di Troia and Katerina Potika and Mark Stamp
- Abstract summary: Spam can be defined as unsolicited bulk email.
In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam.
We apply convolutional neural networks (CNN) to this problem, we compare the results obtained using CNNs to other machine learning techniques, and we compare our results to previous related work.
- Score: 4.817429789586127
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Spam can be defined as unsolicited bulk email. In an effort to evade
text-based filters, spammers sometimes embed spam text in an image, which is
referred to as image spam. In this research, we consider the problem of image
spam detection, based on image analysis. We apply convolutional neural networks
(CNN) to this problem, we compare the results obtained using CNNs to other
machine learning techniques, and we compare our results to previous related
work. We consider both real-world image spam and challenging image spam-like
datasets. Our results improve on previous work by employing CNNs based on a
novel feature set consisting of a combination of the raw image and Canny edges.
Related papers
- Efficient Neural Network based Classification and Outlier Detection for
Image Moderation using Compressed Sensing and Group Testing [4.2455052426413085]
We propose an approach which exploits this fact to reduce the overall computational cost of such engines.
We present the quantitative matrix-pooled neural network (QMPNN), which takes as input $n$ images.
We also present pooled deep outlier detection, which brings CS and group testing techniques to deep outlier detection.
arXiv Detail & Related papers (2023-05-12T17:48:05Z) - Decoupled Mixup for Generalized Visual Recognition [71.13734761715472]
We propose a novel "Decoupled-Mixup" method to train CNN models for visual recognition.
Our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions to train CNN models.
Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts.
arXiv Detail & Related papers (2022-10-26T15:21:39Z) - A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail [5.182080825408661]
A few studies have been conducted with the goal of detecting hybrid spam e-mails.
Optical Character Recognition is a very successful technique in processing text-and-image hybrid spam.
We propose new late multi-modal fusion training frameworks for a text-and-image hybrid spam e-mail filtering system.
arXiv Detail & Related papers (2022-10-26T10:47:12Z) - Explainable Artificial Intelligence to Detect Image Spam Using
Convolutional Neural Network [5.182080825408661]
This research presents an explainable framework for detecting spam images using Convolutional Neural Network(CNN) algorithms and Explainable Artificial Intelligence (XAI) algorithms.
The experimental results show that the proposed framework achieved satisfactory detection results in terms of different performance metrics.
arXiv Detail & Related papers (2022-09-07T14:02:16Z) - Deep Image Deblurring: A Survey [165.32391279761006]
Deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image.
Recent advances in deep learning have led to significant progress in solving this problem.
arXiv Detail & Related papers (2022-01-26T01:31:30Z) - Image Quality Assessment using Contrastive Learning [50.265638572116984]
We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem.
We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models.
Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
arXiv Detail & Related papers (2021-10-25T21:01:00Z) - Universal Adversarial Perturbations and Image Spam Classifiers [4.111899441919165]
Image spam is email that has been embedded in an image.
Modern deep learning-based classifiers perform well in detecting typical image spam.
We propose and analyze a new transformation-based adversarial attack that enables us to create tailored "natural perturbations" in image spam.
arXiv Detail & Related papers (2021-03-07T14:36:02Z) - Image Restoration by Deep Projected GSURE [115.57142046076164]
Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution.
We propose a new image restoration framework that is based on minimizing a loss function that includes a "projected-version" of the Generalized SteinUnbiased Risk Estimator (GSURE) and parameterization of the latent image by a CNN.
arXiv Detail & Related papers (2021-02-04T08:52:46Z) - SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using
Stein's Unbiased Risk Estimate [51.67813146731196]
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems.
CNNs are difficult-to-understand black-boxes.
This limitation is a major barrier to their use in safety-critical applications like medical imaging.
arXiv Detail & Related papers (2020-10-25T20:29:41Z) - Shape Defense Against Adversarial Attacks [47.64219291655723]
Humans rely heavily on shape information to recognize objects. Conversely, convolutional neural networks (CNNs) are biased more towards texture.
Here, we explore how shape bias can be incorporated into CNNs to improve their robustness.
Two algorithms are proposed, based on the observation that edges are invariant to moderate imperceptible perturbations.
arXiv Detail & Related papers (2020-08-31T03:23:59Z) - DeepCapture: Image Spam Detection Using Deep Learning and Data
Augmentation [16.488574089293326]
We propose a new image spam email detection tool called DeepCapture using a convolutional neural network (CNN) model.
DeepCapture is capable of achieving an F1-score of 88%, which has a 6% improvement over the best existing spam detection model CNN-SVM.
arXiv Detail & Related papers (2020-06-16T02:50:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.