Robust Identity Perceptual Watermark Against Deepfake Face Swapping

Notwithstanding offering convenience and entertainment to society, Deepfake face swapping has caused critical privacy issues with the rapid development of deep generative models. Due to imperceptible artifacts in high-quality synthetic images, passive detection models against face swapping in recent years usually suffer performance damping regarding the generalizability issue. Therefore, several studies have been attempted to proactively protect the original images against malicious manipulations by inserting invisible signals in advance. However, the existing proactive defense approaches demonstrate unsatisfactory results with respect to visual quality, detection accuracy, and source tracing ability. In this study, we propose the first robust identity perceptual watermarking framework that concurrently performs detection and source tracing against Deepfake face swapping proactively. We assign identity semantics regarding the image contents to the watermarks and devise an unpredictable and unreversible chaotic encryption system to ensure watermark confidentiality. The watermarks are encoded and recovered by jointly training an encoder-decoder framework along with adversarial image manipulations. Extensive experiments demonstrate state-of-the-art performance against Deepfake face swapping under both cross-dataset and cross-manipulation settings.Comment: Submitted for revie

No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution

Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms' SCM strategies. In this work, we aim at designing online learning algorithms for this problem with an unknown demand distribution, which brings distinct features as compared to classic online optimization problems. Specifically, we consider the two-echelon supply chain model introduced in [Cachon and Zipkin, 1999] under two different settings: the centralized setting, where a planner decides both agents' strategy simultaneously, and the decentralized setting, where two agents decide their strategy independently and selfishly. We design algorithms that achieve favorable guarantees for both regret and convergence to the optimal inventory decision in both settings, and additionally for individual regret in the decentralized setting. Our algorithms are based on Online Gradient Descent and Online Newton Step, together with several new ingredients specifically designed for our problem. We also implement our algorithms and show their empirical effectiveness

Analysis on the Business Model of Fresh E-commerce------Taking Hema Supermarket as an Example

Enterprises are beginning to involve the fresh produce industry, but most companies have withdrawn from the fresh produce industry due to poor performance. This shows that there are many problems with e-commerce of fresh produce. In particular, the business model of e-commerce for fresh produce is a major factor constraining its development. This article takes Hema Supermarket as an example to analyze its business model. It summarizes the areas that can be used for product control, power distribution system construction, platform operation, etc., and provides reference and reference for the operation of fresh agricultural products

Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features

Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To address this problem, we propose Expressive-VC, a novel end-to-end voice conversion framework that leverages advantages from both neural bottleneck feature (BNF) approach and information perturbation approach. Specifically, we use a BNF encoder and a Perturbed-Wav encoder to form a content extractor to learn linguistic and para-linguistic features respectively, where BNFs come from a robust pre-trained ASR model and the perturbed wave becomes speaker-irrelevant after signal perturbation. We further fuse the linguistic and para-linguistic features through an attention mechanism, where speaker-dependent prosody features are adopted as the attention query, which result from a prosody encoder with target speaker embedding and normalized pitch and energy of source speech as input. Finally the decoder consumes the integrated features and the speaker-dependent prosody feature to generate the converted speech. Experiments demonstrate that Expressive-VC is superior to several state-of-the-art systems, achieving both high expressiveness captured from the source speech and high speaker similarity with the target speaker; meanwhile intelligibility is well maintained

Mechanisms and effects of under-ice warming water in Ngoring Lake of Qinghai-Tibet Plateau

Peer reviewe

Structure and mechanism of a methyl transferase ribozyme

Known ribozymes in contemporary biology perform a limited range of chemical catalysis, but in vitro selection has generated species that catalyze a broader range of chemistry; yet, there have been few structural and mechanistic studies of selected ribozymes. A ribozyme has recently been selected that can catalyze a site-specific methyl transfer reaction. We have solved the crystal structure of this ribozyme at a resolution of 2.3 Å, showing how the RNA folds to generate a very specific binding site for the methyl donor substrate. The structure immediately suggests a catalytic mechanism involving a combination of proximity and orientation and nucleobase-mediated general acid catalysis. The mechanism is supported by the pH dependence of the rate of catalysis. A selected methyltransferase ribozyme can thus use a relatively sophisticated catalytic mechanism, broadening the range of known RNA-catalyzed chemistry. [Image: see text