Robustness of Electrical Network Frequency Signals as a Fingerprint for Digital Media Authentication

Poredi, Nihal; Nagothu, Deeraj; Chen, Yu; Li, Xiaohua; Aved, Alexander; Ardiles-Cruz, Erika; Blasch, Erik

doi:10.1109/mmsp55362.2022.9949315

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…10,33 Since, the underlying fingerprint ENF is randomly fluctuating and unique, so as a result, it is harder for any adversary to recreate the fluctuation patterns. 34 ENF is effective for continuous recordings since the targeted frequency requires a minimum sampling rate, which is challenging for single imagebased frequency analysis and renders ENF-based detection a very challenging problem. However, frequency analysis using the Fourier transform has been successful in detecting GAN fingerprints and has been successful in detecting some diffusion model fingerprints.…”

Section: Related Workmentioning

confidence: 99%

AUSOME: authenticating social media images using frequency analysis

Poredi,

Nagothu,

Chen

2023

Disruptive Technologies in Information Sciences VII

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Ever since human society entered the age of social media, every user has had a considerable amount of visual content stored online and shared in variant virtual communities. As an efficient information circulation measure, disastrous consequences are possible if the contents of images are tampered with by malicious actors. Specifically, we are witnessing the rapid development of machine learning (ML) based tools like DeepFake apps. They are capable of exploiting images on social media platforms to mimic a potential victim without their knowledge or consent. These content manipulation attacks can lead to the rapid spread of misinformation that may not only mislead friends or family members but also has the potential to cause chaos in public domains. Therefore, robust image authentication is critical to detect and filter off manipulated images. In this paper, we introduce a system that accurately AUthenticates SOcial MEdia images (AUSOME) uploaded to online platforms leveraging spectral analysis and ML. Images from DALL-E 2 are compared with genuine images from the Stanford image dataset. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used to perform a spectral comparison. Additionally, based on the differences in their frequency response, an ML model is proposed to classify social media images as genuine or AI-generated. Using real-world scenarios, the AUSOME system is evaluated on its detection accuracy. The experimental results are encouraging and they verified the potential of the AUSOME scheme in social media image authentications.

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Section: Related Workmentioning

confidence: 99%

AUSOME: authenticating social media images using frequency analysis

Poredi,

Nagothu,

Chen

2023

Disruptive Technologies in Information Sciences VII

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“…3 At a time when surveillance is increasingly using images and videos, [4][5][6][7] ensuring the authenticity of visual content has become ever important. While deep faked videos have been proven to be detectable through techniques like Electrical Network Frequency, 8,9 AI-generated imagery has not been detectable using the same techniques.…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial networks-based AI-generated imagery authentication using frequency domain analysis

Poredi,

Sudarsan,

Solomon

et al. 2024

Disruptive Technologies in Information Sciences VIII

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In an era characterized by the prolific generation of digital imagery through advanced artificial intelligence, the need for reliable methods to authenticate actual photographs from AI-generated ones has become paramount. The ever-increasing ubiquity of AI-generated imagery, which seamlessly blends with authentic photographs, raises concerns about misinformation and trustworthiness. Authenticating these images has taken on critical significance in various domains, including journalism, forensic science, and social media. Traditional methods of image authentication often struggle to adapt to the increasingly sophisticated nature of AI-generated content. In this context, frequency domain analysis emerges as a promising avenue due to its effectiveness in uncovering subtle discrepancies and patterns that are less apparent in the spatial domain. Delving into the imperative task of imagery authentication, this paper introduces a novel Generative Adversarial Networks (GANs) based AI-generated Imagery Authentication (GANIA) method using frequency domain analysis. By exploiting the inherent differences in frequency spectra, GANIA uncovers unique signatures that are difficult to replicate, ensuring the integrity and authenticity of visual content. By training GANs on vast datasets of real images, we create AI-generated counterparts that closely mimic the characteristics of authentic photographs. This approach enables us to construct a challenging and realistic dataset, ideal for evaluating the efficacy of frequency domain analysis techniques in image authentication. Our work not only highlights the potential of frequency domain analysis for image authentication but also underscores the importance of adopting generative AI approaches in studying this critical topic. Through this innovative fusion of AI and frequency domain analysis, we contribute to advancing image forensics and preserving trust in visual information in an AI-driven world.

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“…While numerous methods [7], [8] have been developed to detect audio fakes, ENF extraction stands out as a distinctive and powerful forensic tool in this domain. The use of ENF as an authentication tool [9], [10] has proven to be highly effective in verifying the authenticity of multimedia record-ings, offering a robust method to combat deep fake attacks and ensure the integrity of digital content. ENF has been exploited in multimedia forensics and anti-forensics analysis [11], enabling timestamp verification [12], [13], [14] and geolocation estimation [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Electric Network Frequency Estimation for Audio Authentication

Korgialas,

Kotropoulos,

Plataniotis

2024

IEEE Access

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The Electric Network Frequency (ENF) serves as a simple means to verify the authenticity of audio recordings. ENF variations contain crucial information, acting as a distinctive "fingerprint" when electronic devices are connected or located near power mains. A novel framework for ENF estimation is proposed. This approach alternates between the Least Absolute Deviation (LAD) regression for determining regression weights and objective function minimization with respect to frequency, adapting them within the context of the ℓ 1 norm or the sum of ℓ 1 norms of the approximation error. This framework is a direct consequence of Laplacian distributed noise. Goodness-of-fit tests are reported, indicating that the Laplacian noise hypothesis is more appropriate than the hypothesis of Gaussian noise in the benchmark ENF-WHU dataset. Extensive evaluation using audio recordings from the aforementioned dataset demonstrates the exceptional performance of the proposed framework outperforming state-of-the-art ENF estimation schemes. These findings provide compelling evidence for the efficacy of the proposed ENF estimation schemes as reliable prerequisites for detecting audio forgeries.

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Robustness of Electrical Network Frequency Signals as a Fingerprint for Digital Media Authentication

Cited by 6 publications

References 18 publications

AUSOME: authenticating social media images using frequency analysis

AUSOME: authenticating social media images using frequency analysis

Generative adversarial networks-based AI-generated imagery authentication using frequency domain analysis

Leveraging Electric Network Frequency Estimation for Audio Authentication

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