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Register a new userFakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking
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Added by
Run Wang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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In recent years, DeepFake is becoming a common threat to our society, due to the remarkable progress of generative adversarial networks (GAN) in image synthesis. Unfortunately, existing studies that propose various approaches, in fighting against DeepFake and determining if the facial image is real or fake, is still at an early stage. Obviously, the current DeepFake detection method struggles to catch the rapid progress of GANs, especially in the adversarial scenarios where attackers can evade the detection intentionally, such as adding perturbations to fool the DNN-based detectors. While passive detection simply tells whether the image is fake or real, DeepFake provenance, on the other hand, provides clues for tracking the sources in DeepFake forensics. Thus, the tracked fake images could be blocked immediately by administrators and avoid further spread in social networks.In this paper, we investigate the potentials of image tagging in serving the DeepFake provenance tracking. Specifically, we devise a deep learning-based approach, named FakeTagger, with a simple yet effective encoder and decoder design along with channel coding to embed message to the facial image, which is to recover the embedded message after various drastic GAN-based DeepFake transformation with high confidence. Experimental results demonstrate that our proposed approach could recover the embedded message with an average accuracy of more than 95% over the four common types of DeepFakes. Our research finding confirms effective privacy-preserving techniques for protecting personal photos from being DeepFaked. Thus, effective proactive defense mechanisms should be developed for fighting against DeepFakes, instead of simply devising DeepFake detection methods that can be mostly ineffective in practice.
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Current state-of-the-art trackers often fail due to distractorsand large object appearance changes. In this work, we explore the use ofdense optical flow to improve tracking robustness. Our main insight is that, because flow estimation can also have errors, we need to incorporate an estimate of flow uncertainty for robust tracking. We present a novel tracking framework which combines appearance and flow uncertainty information to track objects in challenging scenarios. We experimentally verify that our framework improves tracking robustness, leading to new state-of-the-art results. Further, our experimental ablations shows the importance of flow uncertainty for robust tracking.
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For data-centric systems, provenance tracking is particularly important when the system is open and decentralised, such as the Web of Linked Data. In this paper, a concise but expressive calculus which models data updates is presented. The calculus is used to provide an operational semantics for a system where data and updates interact concurrently. The operational semantics of the calculus also tracks the provenance of data with respect to updates. This provides a new formal semantics extending provenance diagrams which takes into account the execution of processes in a concurrent setting. Moreover, a sound and complete model for the calculus based on ideals of series-parallel DAGs is provided. The notion of provenance introduced can be used as a subjective indicator of the quality of data in concurrent interacting systems.
The Spectre vulnerability in modern processors has been widely reported. The key insight in this vulnerability is that speculative execution in processors can be misused to access the secrets. Subsequently, even though the speculatively executed instructions are squashed, the secret may linger in micro-architectural states such as cache, and can potentially be accessed by an attacker via side channels. In this paper, we propose oo7, a static analysis approach that can mitigate Spectre attacks by detecting potentially vulnerable code snippets in program binaries and protecting them against the attack by patching them. Our key contribution is to balance the concerns of effectiveness, analysis time and run-time overheads. We employ control flow extraction, taint analysis, and address analysis to detect tainted conditional branches and speculative memory accesses. oo7 can detect all fifteen purpose-built Spectre-vulnerable code patterns, whereas Microsoft compiler with Spectre mitigation option can only detect two of them. We also report the results of a large-scale study on applying oo7 to over 500 program binaries (average binary size 261 KB) from different real-world projects. We protect programs against Spectre attack by selectively inserting fences only at vulnerable conditional branches to prevent speculative execution. Our approach is experimentally observed to incur around 5.9% performance overheads on SPECint benchmarks.
System level provenance is of widespread interest for applications such as security enforcement and information protection. However, testing the correctness or completeness of provenance capture tools is challenging and currently done manually. In some cases there is not even a clear consensus about what behavior is correct. We present an automated tool, ProvMark, that uses an existing provenance system as a black box and reliably identifies the provenance graph structure recorded for a given activity, by a reduction to subgraph isomorphism problems handled by an external solver. ProvMark is a beginning step in the much needed area of testing and comparing the expressiveness of provenance systems. We demonstrate ProvMarks usefuless in comparing three capture systems with different architectures and distinct design philosophies.
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