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Bias Variance@biasvariance_ir P.153
BIASVARIANCE_IR Telegram 153
یکی از فناوری هایی که در سالهای اخیر به وجود آمد و سر و صدای بسیاری کرد، deep fake بود. با استفاده از مدلهایی که در این زمینه هستند، می توان صدا، عکس، ویدیو و ... ساخت که در حقیقت وجود نداشته اند. یکی از چالشهای موجود این است که روشهایی داشته باشیم که بفهمیم آیا سیگنال ورودی با deep fake ساخته شده است یا خیر. در ادامه مقاله ای که چند وقت پیش ارایه شد پیرامون این زمینه را قرار می دهیم. ساخت مدلهایی که بر مبنای deep fake هستند و شناسایی خروجی های مبتنی بر deep fake یکی از شاخه های بسیار جالب برای نوشتن پایان نامه در ارشد و دکتری است. جالب است بدانید در زمینه های تصویر، صوت، ویدیو و ... می توان بر روی این حوزه کار کرد.

An Experimental Evaluation on Deepfake Detection using Deep Face Recognition

Significant advances in deep learning have obtained hallmark accuracy rates for various computer vision applications. However, advances in deep generative models have also led to the generation of very realistic fake content, also known as deepfakes, causing a threat to privacy, democracy, and national security. Most of the current deepfake detection methods are deemed as a binary classification problem in distinguishing authentic images or videos from fake ones using two-class convolutional neural networks (CNNs). These methods are based on detecting visual artifacts, temporal or color inconsistencies produced by deep generative models. However, these methods require a large amount of real and fake data for model training and their performance drops significantly in cross dataset evaluation with samples generated using advanced deepfake generation techniques. In this paper, we thoroughly evaluate the efficacy of deep face recognition in identifying deepfakes, using different loss functions and deepfake generation techniques. Experimental investigations on challenging Celeb-DF and FaceForensics++ deepfake datasets suggest the efficacy of deep face recognition in identifying deepfakes over two-class CNNs and the ocular modality. Reported results suggest a maximum Area Under Curve (AUC) of 0.98 and an Equal Error Rate (EER) of 7.1% in detecting deepfakes using face recognition on the Celeb-DF dataset. This EER is lower by 16.6% compared to the EER obtained for the two-class CNN and the ocular modality on the Celeb-DF dataset. Further on the FaceForensics++ dataset, an AUC of 0.99 and EER of 2.04% were obtained. The use of biometric facial recognition technology has the advantage of bypassing the need for a large amount of fake data for model training and obtaining better generalizability to evolving deepfake creation techniques.

#معرفی_مقاله #deep_fake #یادگیری_عمیق

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یکی از فناوری هایی که در سالهای اخیر به وجود آمد و سر و صدای بسیاری کرد، deep fake بود. با استفاده از مدلهایی که در این زمینه هستند، می توان صدا، عکس، ویدیو و ... ساخت که در حقیقت وجود نداشته اند. یکی از چالشهای موجود این است که روشهایی داشته باشیم که بفهمیم آیا سیگنال ورودی با deep fake ساخته شده است یا خیر. در ادامه مقاله ای که چند وقت پیش ارایه شد پیرامون این زمینه را قرار می دهیم. ساخت مدلهایی که بر مبنای deep fake هستند و شناسایی خروجی های مبتنی بر deep fake یکی از شاخه های بسیار جالب برای نوشتن پایان نامه در ارشد و دکتری است. جالب است بدانید در زمینه های تصویر، صوت، ویدیو و ... می توان بر روی این حوزه کار کرد.

An Experimental Evaluation on Deepfake Detection using Deep Face Recognition

Significant advances in deep learning have obtained hallmark accuracy rates for various computer vision applications. However, advances in deep generative models have also led to the generation of very realistic fake content, also known as deepfakes, causing a threat to privacy, democracy, and national security. Most of the current deepfake detection methods are deemed as a binary classification problem in distinguishing authentic images or videos from fake ones using two-class convolutional neural networks (CNNs). These methods are based on detecting visual artifacts, temporal or color inconsistencies produced by deep generative models. However, these methods require a large amount of real and fake data for model training and their performance drops significantly in cross dataset evaluation with samples generated using advanced deepfake generation techniques. In this paper, we thoroughly evaluate the efficacy of deep face recognition in identifying deepfakes, using different loss functions and deepfake generation techniques. Experimental investigations on challenging Celeb-DF and FaceForensics++ deepfake datasets suggest the efficacy of deep face recognition in identifying deepfakes over two-class CNNs and the ocular modality. Reported results suggest a maximum Area Under Curve (AUC) of 0.98 and an Equal Error Rate (EER) of 7.1% in detecting deepfakes using face recognition on the Celeb-DF dataset. This EER is lower by 16.6% compared to the EER obtained for the two-class CNN and the ocular modality on the Celeb-DF dataset. Further on the FaceForensics++ dataset, an AUC of 0.99 and EER of 2.04% were obtained. The use of biometric facial recognition technology has the advantage of bypassing the need for a large amount of fake data for model training and obtaining better generalizability to evolving deepfake creation techniques.

#معرفی_مقاله #deep_fake #یادگیری_عمیق

🌴 سایت | 🌺 کانال | 🌳 پشتیبانی

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