A Mobile-Oriented System for Integrity Preserving in Audio Forensics

Renza, Diego; Arango, Jaime Andres; Ballesteros, Dora M.

doi:10.3390/app9153097

Cited by 2 publications

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References 19 publications

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“…Multimedia forensics is one of the key technologies for digital evidence authentication in cybersecurity. The rapid expansion in the amount of digital content in social networks has also brought about a significant increase in the number of copyright infringements [1]. Nowadays, new image processing manipulations are rapidly developing and are incorporated into image processing software such as Photo Impact and Adobe Photoshop.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Copy Detection Using Convolutional Neural Network

et al. 2020

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With the rapid development of network technology, concerns pertaining to the enhancement of security and protection against violations of digital images have become critical over the past decade. In this paper, an image copy detection scheme based on the Inception convolutional neural network (CNN) model in deep learning is proposed. The image dataset is transferred by a number of image processing manipulations and the feature values in images are automatically extracted for learning and detecting the suspected unauthorized digital images. The experimental results show that the proposed scheme takes on an extraordinary role in the process of detecting duplicated images with rotation, scaling, and other content manipulations. Moreover, the mechanism of detecting duplicate images via a convolutional neural network model with different combinations of original images and manipulated images can improve the accuracy and efficiency of image copy detection compared with existing schemes.

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Section: Introductionmentioning

confidence: 99%

Content-Based Image Copy Detection Using Convolutional Neural Network

et al. 2020

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“…In any case, how these challenges are addressed depends on the characteristics and availability of information on the content to be evaluated. This is usually achieved through the calculation of a numerical value based on input data, i.e., the use of cryptographic hash functions [14]. However, due to the high sensitivity of these functions to minor changes in the input, this solution is not practical when exact copies of the content to be evaluated are not available [5].…”

Section: Introductionmentioning

confidence: 99%

Robust Speech Hashing for Digital Audio Forensics

2019

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The verification of the integrity and authenticity of multimedia content is an essential task in the forensic field, in order to make digital evidence admissible. The main objective is to establish whether the multimedia content has been manipulated with significant changes to its content, such as the removal of noise (e.g., a gunshot) that could clarify the facts of a crime. In this project we propose a method to generate a summary value for audio recordings, known as hash. Our method is robust, which means that if the audio has been modified slightly (without changing its significant content) with perceptual manipulations such as MPEG-4 AAC, the hash value of the new audio is very similar to that of the original audio; on the contrary, if the audio is altered and its content changes, for example with a low pass filter, the new hash value moves away from the original value. The method starts with the application of MFCC (Mel-frequency cepstrum coefficients) and the reduction of dimensions through the analysis of main components (principal component analysis, PCA). The reduced data is encrypted using as inputs two values from a particular binarization system using Collatz conjecture as the basis. Finally, a robust 96-bit code is obtained, which varies little when perceptual modifications are made to the signal such as compression or amplitude modification. According to experimental tests, the BER (bit error rate) between the hash value of the original audio recording and the manipulated audio recording is low for perceptual manipulations, i.e., 0% for FLAC and re-quantization, 1% in average for volume (−6 dB gain), less than 5% in average for MPEG-4 and resampling (using the FIR anti-aliasing filter); but more than 25% for non-perceptual manipulations such as low pass filtering (3 kHz, fifth order), additive noise, cutting and copy-move.

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A Mobile-Oriented System for Integrity Preserving in Audio Forensics

Cited by 2 publications

References 19 publications

Content-Based Image Copy Detection Using Convolutional Neural Network

Content-Based Image Copy Detection Using Convolutional Neural Network

Robust Speech Hashing for Digital Audio Forensics

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