Multiscale Attention Network for Detection and Localization of Image Splicing Forgery

Xu, Yanzhi; Irfan, Muhammad; Fang, Aiqing; Zheng, Jiangbin

doi:10.1109/tim.2023.3300434

Cited by 5 publications

(2 citation statements)

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“…Although the methods discussed above can achieve good accuracy, most require artificial feature engineering, which involves massive human professional knowledge and is usually a very time-consuming process. In recent years, deep learning has made significant progress in machine learning and has been widely applied in various fields such as digital image recognition, speech recognition, and steganography analysis [20][21][22][23][24]. Among them, Xu et al [20] proposed a multiscale attention network for splicing tampering forensics in image recognition, which utilizes the integration of residual attention and multiscale information in order to improve the detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, deep learning has made significant progress in machine learning and has been widely applied in various fields such as digital image recognition, speech recognition, and steganography analysis [20][21][22][23][24]. Among them, Xu et al [20] proposed a multiscale attention network for splicing tampering forensics in image recognition, which utilizes the integration of residual attention and multiscale information in order to improve the detection accuracy. Lang et al [21] applied deep learning technology to the field of industrial defect detection, aiming to improve the accuracy of magnetic flux leakage (MFL) image recognition of pipeline corrosion defects, and achieved remarkable results.…”

Section: Introductionmentioning

confidence: 99%

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Pyramid Feature Attention Network for Speech Resampling Detection

Zhou,

Zhang,

Wang

et al. 2024

Applied Sciences

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Speech forgery and tampering, increasingly facilitated by advanced audio editing software, pose significant threats to the integrity and privacy of digital speech avatars. Speech resampling is a post-processing operation of various speech-tampering means, and the forensic detection of speech resampling is of great significance. For speech resampling detection, most of the previous works used traditional methods of feature extraction and classification to distinguish original speech from forged speech. In view of the powerful ability of deep learning to extract features, this paper converts the speech signal into a spectrogram with time-frequency characteristics, and uses the feature pyramid network (FPN) with the Squeeze and Excitation (SE) attention mechanism to learn speech resampling features. The proposed method combines the low-level location information and the high-level semantic information, which dramatically improves the detection performance of speech resampling. Experiments were carried out on a resampling corpus made on the basis of the TIMIT dataset. The results indicate that the proposed method significantly improved the detection accuracy of various resampled speech. For the tampered speech with a resampling factor of 0.9, the detection accuracy is increased by nearly 20%. In addition, the robustness test demonstrates that the proposed model has strong resistance to MP3 compression, and the overall performance is better than the existing methods.

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