Robust and Calibrated Detection of Authentic Multimedia Content
Generative models can synthesize highly realistic content, so-called deepfakes, that are already being misused at scale to undermine digital media authenticity. Current deepfake detection methods are unreliable for two reasons: (i) distinguishing inauthentic content post-hoc is often impossible (e.g., with memorized samples), leading to an unbounded false positive rate (FPR); and (ii) detection lacks robustness, as adversaries can adapt to known detectors with near-perfect accuracy using minimal computational resources. To address these limitations, we propose a resynthesis framework to determine if a sample is authentic or if its authenticity can be plausibly denied. We make two key contributions focusing on the high-precision, low-recall setting against efficient (i.e., compute-restricted) adversaries. First, we demonstrate that our calibrated resynthesis method is the most reliable approach for verifying authentic samples while maintaining controllable, low FPRs. Second, we show that our method achieves adversarial robustness against efficient adversaries, whereas prior methods are easily evaded under identical compute budgets. Our approach supports multiple modalities and leverages state-of-the-art inversion techniques.
69437830542d62d58a7bf752
Sarim Hashmi
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2025-12-18T07:59:45.380Z
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SARIM HASHMI
Sarim-Hash
user
Sarim-Hash
69437830542d62d58a7bf753
Abdelrahman Elsayed
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2025-12-19T09:07:02.236Z
66f7e7a78d215c6331aabee2
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Abdelrahman Amr El Sayed
pythn
user
pythn
69437830542d62d58a7bf754
Mohammed Talha Alam
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2025-12-19T09:07:05.214Z
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Mohammed Talha Alam
talha-alam
user
talha-alam
69437830542d62d58a7bf755
Samuele Poppi
69437830542d62d58a7bf756
Nils Lukas
Robust and Calibrated Detection of Authentic Multimedia Content
Generative models can synthesize highly realistic content, so-called deepfakes, that are already being misused at scale to undermine digital media authenticity. Current deepfake detection methods are unreliable for two reasons: (i) distinguishing inauthentic content post-hoc is often impossible (e.g., with memorized samples), leading to an unbounded false positive rate (FPR); and (ii) detection lacks robustness, as adversaries can adapt to known detectors with near-perfect accuracy using minimal computational resources. To address these limitations, we propose a resynthesis framework to determine if a sample is authentic or if its authenticity can be plausibly denied. We make two key contributions focusing on the high-precision, low-recall setting against efficient (i.e., compute-restricted) adversaries. First, we demonstrate that our calibrated resynthesis method is the most reliable approach for verifying authentic samples while maintaining controllable, low FPRs. Second, we show that our method achieves adversarial robustness against efficient adversaries, whereas prior methods are easily evaded under identical compute budgets. Our approach supports multiple modalities and leverages state-of-the-art inversion techniques.
A resynthesis framework enhances deepfake detection by verifying authenticity with low false positive rates and robustness against efficient adversaries, supporting multiple modalities.
deepfakes deepfake detection resynthesis framework false positive rate adversarial robustness inversion techniques