JPEG image classification in digital forensic via DCT coefficient analysis

Alherbawi, Nadeem; Shukur, Zarina; Sulaiman, Riza

doi:10.1007/s11042-017-4915-3

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The authors of [90] proposed a model for efficiently and accurately identifying the forensic process by classifying bulk JPEG images generated by the data engraving process or other means. JPEG images with errors or corrupted data fall into the first classification.…”

Section: Forensic Methods For Handling Image Filesmentioning

confidence: 99%

Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems

et al. 2021

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In recent years, there has been a dramatic change in attitude towards computers and the use of computer resources in general. Cloud and Edge computing have emerged as the most widely used technologies, including fog computing and the Internet of Things (IoT). There are several benefits in exploiting Cloud and Edge computing paradigms, such as lower costs and higher efficiency. It provides data computation and storage where data are processed, enables better data control, faster understanding and actions, and continuous operation. However, though these benefits seem to be appealing, their effects on computer forensics are somewhat undesirable. The complexity of the Cloud and Edge environments and their key features present many technical challenges from multiple stakeholders. This paper seeks to establish an in-depth understanding of the impact of Cloud and Edge computing-based environmental factors. Software and hardware tools used in the digital forensic process, forensic methods for handling tampered sound files, hidden files, image files, or images with steganography, etc. The technical/legal challenges and the open design problems (such as distributed maintenance, multitasking and practicality) highlight the various challenges for the digital forensics process.

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Section: Forensic Methods For Handling Image Filesmentioning

confidence: 99%

Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems

et al. 2021

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“…For example, it is still difficult to form a complex and accurate flavor through a simple concentration of the proportion of chemical raw materials. ere are far from enough scenes of smell and taste in the existing entertainment scenes; therefore, it is difficult to form a commercial development [20]. is paper will take the stereo simulation of the auditory system as an example.…”

Section: Elements Of the Yuan Universementioning

confidence: 99%

Metaverse Space Ecological Scene Design Based on Multimedia Digital Technology

Zou

Chen

et al. 2022

Mobile Information Systems

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Yuan cosmos is a virtual world linked and created by scientific and technological means, which is mapped and interacted with the real world, and a digital living space with a new social system. With the increasing popularity of data acquisition and production equipment, people are increasingly convenient to produce multimedia data such as images, graphics, audio, video, animation, and three-dimensional models. In addition to the rapid development of digital technology itself, the biological information technology related to digital technology also greatly promotes the emergence of the metauniverse. This paper aims to study the application of multimedia digital technology to the ecological scene design of metauniverse space, introduces the related concepts of metauniverse and multimedia digital technology, expounds the related methods of multimedia digital technology and neural network related algorithms, and then takes the three-dimensional simulation of the auditory system in the interactive multisensory simulation system of the constituent elements of metauniverse as an example. The mel-frequency cepstrum coefficient (MFCC) is used to simulate the auditory characteristics of the auditory periphery (cochlea) as the perceptual end of the model. A variety of bionic mechanisms are used in the model, such as designing the connection mode of neurons, learning state and release effect, and the regeneration mechanism of neurons. For the verification of the performance of the model, the speech sample database, including English words and phrases, is recorded and the speech content information recognized by the model by means of speech recognition is experimented. The experimental results show that, in terms of phrase accuracy, the DN-1 model improves 2.59% and the DN-2 model improves 2.77% compared with MFCC feature on the basis of mixed features. When only DBN features are used, the performance improvement rate of the developmental network model is small.

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