An intelligent character recognition method to filter spam images on cloud

Abstract

Cloud storage has become an important way for data sharing in recent years. Data protection for data owner and harmful data filtering for data recipients are two nonnegligible problems in cloud storage. Illegal or unsuitable messages on cloud have a negative impact on minors and they are easily converted into images to avoid text-based filtering. To detect the spam image with the embedded harmful messages on cloud, soft computing methods are required for intelligent character recognition. HOG, proposed by Dalal and Triggs, has been demonstrated so far to be one of the best features for intelligent character recognition. A pre-defined sliding window is always used for the generation of candidate character images when HOG is applied to recognize the whole word. However, due to the difference in character sizes, the pre-defined window cannot exactly match with each character. Variations on scale and translation usually occur in the character image to be recognized, which have a great influence on the performance of intelligent character recognition. Aiming to solve this problem, STRHOG, an extended version of HOG, is proposed in this paper. Experiments on two public datasets and one our dataset have shown encouraging results for our work. The improved intelligent character recognition is helpful for filtering spam images on cloud. To make a fair comparison with other methods, nearest neighbor classifier is used for the intelligent character recognition. It is expected that the performance should be further improved by using better classifiers such as fuzzy neural network.

Phishing Image Spam Classification Research Trends: Survey and Open Issues

“…Text-based 9 [18], [19], [20], [21], [22], [23], [24], [25], [26] Image-based 16 [27], [28], [29], [9], [30], [31], [32], [33], [34], [35], [17], [5], [36], [37], [38], [39] [20], [30], [34], [5], [37], [38], [39] Dredze 10 5789 spam (spam =3239 & ham = 2550) [30], [31], [33], [34], [40], [17], [5], [37], [28], [21] Enron corpus 1 Not specified [22] SMS spam 1 Not specified [18] Princeton spam corpus [20], [24], [30], [31], [41], [32],…”

“…Naiemi et al [9] proposed a new algorithm to recognize characters in image spam by improving the existing feature extraction of HOG using SVM as the classifier. The study improved scale and translation robust HOG (STRHOG) developed with the Chars74K dataset with an accuracy of 72.2% [29]. In STRHOG, the matrices of the oriented gradient for input images of different sizes have a high computation value and a large part of this matrix does not have any effect in recognizing the image.…”

“…Also, the authors revealed that fog-based services are faster than cloud-based services and that it is manageable and easy [20], [31], [32], [33], [34], [35], [17], [27], [9], [44] 4. K-Nearest Neighbor 3 y y [22], [28], [29], [44] 5 to implement a machine learning algorithm on fog nodes than on the cloud. In view of this, an algorithm can be implemented on a fog node to increase the detection speed of the image spam classification.…”