KEYWORDS: Databases, Forensic science, Data storage, Digital forensics, Computer security, Data modeling, Mobile devices, Data conversion, Data communications, Analytical research
With the rapid development of information technology, digitalization has gradually integrated into the daily lives of modern people, becoming an indispensable and important component. Especially, DingTalk software, as an office application that integrates communication and collaboration, has been widely used in daily office work. This paper studies the authenticity of electronic data produced by DingTalk software and its evidence collection methods. By analyzing the data storage mechanism of DingTalk, it identifies the challenges faced by electronic data evidence collection and proposes a method to ensure the authenticity of data. Meanwhile, it discusses strategies to counter data tampering techniques. The paper ultimately offers suggestions to improve the accuracy of evidence collection, aimed at ensuring the legal effectiveness of electronic evidence to meet security challenges and evidence collection needs.
KEYWORDS: Forensic science, Databases, Data storage, Error control coding, Data acquisition, Analytical research, Data modeling, Computer security, Lithium, Digital forensics
The rapid growth in usage and Instant Messenger Applications (IMAs) has made them a potential target by cyber criminals to conduct malicious activities such as identity theft, illegal trading, etc. For example, WeChat is extending its services to mobile platforms, making it an important source of evidence in cyber investigation cases. Therefore, understanding the types of potential evidence of users activities available on mobile devices is crucial for forensic investigations and research. In this article, we examined WeChat, the most popular application on the Android platform. We created various artifacts (e.g., texts messages, images, audio, and document) that are of forensic interest to perform targeted spoofing tampering with WeChat data. This helps in analyzing the authenticity of WeChat data during forensics, thereby proving the innocence of people or the existence of certain criminal activities.
Powerful image editing software makes the process of image manipulation easy, which increases security risks. Therefore, it is urgent to locate the tampered region to uncover the processing history. However, previous research has mainly focused on feature extraction, with little discussion on classifiers for classifying original and tampered regions. We improve the splicing forgery localization method from a statistical perspective. The refined color filter array feature provides sufficient data for statistical analysis, and the geometric mean is used to eliminate anomalous data. Subsequently, a classifier that combines the expectation–maximization algorithm and Bayesian theory is proposed to binarize the original and tampered regions. The two steps of feature extraction and feature classification are associated from a statistical perspective, which ultimately improve the performance of the method effectively. Extensive experimental results demonstrate that the refined feature used for classification has several advantages, and the proposed classifier is appropriate for handling complex image manipulation across different statistical distributions. The proposed method outperforms the reference methods in both the Columbia and Korus datasets.
This paper proposes a study on whether the speaker’s body size (height, weight) and oral cavity (lip protrusion LP, lip opening LO, front cavity FC) characteristics can be predicted based on the acoustic features of speech. Firstly, Pearson’s correlation analysis was first conducted to examine the relationships between acoustic features and body size and oral cavity characteristics. Further, the effects of acoustic features in predicting body size and oral cavity characteristics were examined using random forest and decision tree models. The results showed that fundamental frequency statistics (i.e., mean, max, min) exhibited significant negative correlations with height, weight, and FC. Besides, good accuracies of classification in height, LP range, LO range, and FC range could be achieved based on the acoustic features. The findings in the current paper imply that acoustic features could be the potential features for identification of the speaker’s body size and oral cavity characteristics. This paper will not only contribute to the research and practices in forensic speaker profiling and but also provides foundations for the technology of automatic speaker recognition.
With the development of speech synthesis technology, the simulation of specific individual’s speech has gradually matured, synthetic speech is easily and perceptually recognized as real speech, which may occur frequently in illegal activities. To identify crimes, forensic technology is widely used such as comparing the formants, pitches, and rhythm. The present study aims to investigate whether the method of comparison of formants can recognize the differences between the perceptually similar synthetic speech (hereinafter “personal anchor” speech) and real speech. To this end, two young males and two young females from different dialect regions were recruited to read the same text. Their voices were recorded and used to generate four “personal anchors” by the software of sound spectrum and statistics analysis. The method of comparison of various parameters of the formant, including numerical statistical, stability analysis, and transitional segments feature were applied to analyze the differences between the real speech and the corresponding “personal anchors”. It was found that the numerical or stability analysis of formants was not sufficient to fully determine whether the speech was synthesized, while comparing the transitional segments of some specific syllables could efficiently detect the synthetic speech from the real speech.
Using a natural conversation paradigm, this study investigated the acoustic characteristics of Mandarin utterances in drug addicts. Twenty-one native speakers of Mandarin, including four heroin addicts, two 3,4-methylamphetamine (MDMA, also known as ecstasy) addicts, and 15 healthy controls without any history of drug abuse, were recruited for the speech production experiment. In comparison with the healthy controls, heroin addicts exhibited a higher mean F0, a lower mean intensity, a higher variability in both F0 and intensity, and a lower H1-H2, while MDMA addicts exhibited a higher variability in both F0 and intensity. Discriminant analysis based on these acoustic parameters further showed a good accuracy of differentiating the three groups of speakers. These findings provide the basis for future research into identifying drug addicts on the basis of speech signals.
Purpose: The current study aims to investigate the effect of orofacial motions for automatic attitude recognition Method: To achieve this goal, the movements of lips and jaw during the expressions of six common attitudes from 33 native Mandarin Chinese speakers were collected using ElectroMegnatic Articulography. The random forest classifications were then conducted for attitude recognition based on orofacial data. Results: the average rate of attitude recognition was 63.45%, and the identification accuracy was above 60% for almost every attitude. Besides, for the further classifications separately conducting on each pair of attitudes, the opposing attitudes within each attitude pair were all reasonably recognized (i.e., above 65%). Conclusion: The use of orofacial expressions in attitude expression could be valuable features for the technology of automatic attitude recognition.
With the development of internet technology, artificial intelligence shows its rapid growth in recent year as well. More and more people pay attention to this field, including criminals inevitably. It is noted that one technology called “Deepfake” appeared on the internet at the end of 2017. As the name suggests, it is a portmanteau of “deep learning” and “fake”. In essence, Deepfake is a deep-learning framework in the field of image composite and replacement, which swap faces in images or videos. However detection of face forgery is still in its early stages, due to its novelty and complexity. In this paper we will demonstrate some dataset for deepfake forensics firstly, and then describe various existing detection methods. Those methods are reviewed in two parts: detection based on frame sequences and detection based on single frame. The former is implemented by differences between frames including human features, optical flow, timeline and so on, while the latter is based on features of single frame including extracted features and fusion boundary. Various convolutional Neural Networks (abbreviated as CNN) will be illustrated in this paper. Accordingly, performances of above algorithms are likely to be demonstrated and compared, and a further explanation will be given regarding on their applicable dataset. Finally, further research of face forgery detection of deepfake including methods and applications will be discussed.
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