Perceptual image hashing based on frequency dominant neighborhood structure applied to Tor domains recognition

Biswas, Rubel; González-Castro, V́ıctor; Fidalgo, Eduardo; Alegre, Enrique

doi:10.1016/j.neucom.2019.11.065

Cited by 22 publications

(15 citation statements)

References 30 publications

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“…These RPbased methods can resist rotation of large degrees, but their discrimination needs to be improved. Recently, Biswas et al [51] utilized DNS (Dominant Neighborhood Structure) and DCT to extract textural energy map for hash construction. Biswas's method can be used for recognizing Tor domains, but the time complexity is too high.…”

Section: Related Workmentioning

confidence: 99%

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Liang

Tang

Huang

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant lowdimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.

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Section: Related Workmentioning

confidence: 99%

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Liang

Tang

Huang

et al. 2023

IEEE Trans. Knowl. Data Eng.

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“…The researchers in [27] proposed a programmed model that recognizes the activities being done on the Tor network by analyzing the activities' snapshots. The researchers in [12] combined three proficient algorithms with the help of a rectification network to enhance the algorithm's text recognition procedure.…”

Section: Related Workmentioning

confidence: 99%

DarkDetect: Darknet Traffic Detection and Categorization Using Modified Convolution-Long Short-Term Memory

et al. 2021

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Darknet is commonly known as the epicenter of illegal online activities. An analysis of darknet traffic is essential to monitor real-time applications and activities running over the Darknet. Recognizing network traffic bound to unused Internet addresses has become undeniably significant for identifying and examining malicious activities on the internet. Since there are no authentic hosts or devices in an unused address block, any observed network traffic must be the aftereffect of misconfiguration from spoofed source addressed and other frameworks that monitor unused address space. However, the recent advancements in artificial intelligence allow digital systems to detect and identify darknet traffic autonomously. In this paper, we propose a generalized approach for darknet traffic detection and categorization using Deep Learning. We examine the state-of-the-art complex dataset, which provides excessive information about the darknet traffic and perform data preprocessing. Next, we analyze diverse feature selection techniques to select optimal features for darknet traffic detection and categorization. We apply fine-tuned machine learning (ML) algorithms which include Decision Tree (DT), Gradient Boosting (GB), Random Forest Regressor (RFR), and Extreme Gradient Boosting (XGB) on selected features and compare the performance. Next, we apply modified Convolution-Long Short-Term Memory (CNN-LSTM) and Convolution-Gradient Recurrent Unit (CNN-GRU) deep learning techniques to recognize the network traffic more accurately. The results demonstrate that the proposed approach outperforms the existing approaches by yielding the maximum accuracy of 96% of darknet traffic detection and 89% of darknet traffic categorization through XGB as a feature selection approach and CNN-LSTM a recognition model.

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“…The perceptual hashing approach generates a fixed-length fingerprint, i.e., a hash code based on the perceptual content of the image/video/audio. In the last few years, perceptual hashing has been used in different applications such as tampering detection [15], person re-identification [7], victim identification [4], or illegal Tor domain classification [5].…”

Section: Perceptual Hashingmentioning

confidence: 99%

A Video Summarization Approach to Speed-up the Analysis of Child Sexual Exploitation Material

Biswas¹,

Chaves²,

Fernández-Robles³

et al. 2021

Xlii Jornadas De Automática : Libro De Actas

Self Cite

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Identifying key content from a video is essential for many security applications such as motion/action detection, person re-identification and recognition. Moreover, summarizing the key information from Child Sexual Exploitation Materials, especially videos, which mainly contain distinctive scenes including people’s faces is crucial to speed-up the investigation of Law Enforcement Agencies. In this paper, we present a video summarization strategy that combines perceptual hashing and face detection algorithms to keep the most relevant frames of a video containing people’s faces that may correspond to victims or offenders. Due to legal constraints to access Child Sexual Abuse datasets, we evaluated the performance of the proposed strategy during the detection of adult pornography content with the NDPI-800 dataset. Also, we assessed the capability of our strategy to create video summaries preserving frames with distinctive faces from the original video using ten additional short videos manually labeled. Results showed that our approach can detect pornography content with an accuracy of 84.15% at a speed of 8.05 ms/frame making this appropriate for realtime applications.

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Perceptual image hashing based on frequency dominant neighborhood structure applied to Tor domains recognition

Cited by 22 publications

References 30 publications

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

DarkDetect: Darknet Traffic Detection and Categorization Using Modified Convolution-Long Short-Term Memory

A Video Summarization Approach to Speed-up the Analysis of Child Sexual Exploitation Material

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