Biblio

AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.

In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes). Our method is based on the observations that Deep Fakes are created by splicing synthesized face region into the original image, and in doing so, introducing errors that can be revealed when 3D head poses are estimated from the face images. We perform experiments to demonstrate this phenomenon and further develop a classification method based on this cue. Using features based on this cue, an SVM classifier is evaluated using a set of real face images and Deep Fakes.

In this paper, an novel active safety monitoring system is constructed for a class of nonlinear discrete-time systems. The considered nonlinear system is subjected to unknown inputs, external disturbances, and possible unknown deception attacks, simultaneously. In order to secure the safety of control systems, an active attack estimator composed of state/output estimator, attack detector and attack/attacker action estimator is constructed to monitor the system running status. The analysis and synthesis of attack estimator is performed in the H∞performance optimization manner. The off-line calculation and on-line application of active attack estimator are summarized simultaneously. The effectiveness of the proposed results is finally verified by an numerical example.

We propose a secure key generation and distribution scheme for data encryption in classical optical fiber channel. A Delay interferometer (DI) is used to track the random phase fluctuation inside fiber, while the reconfigurable lengths of polarization-maintaining (PM) fiber are set as the source of optical phase fluctuations. The output signals from DI are extracted as the secret key and shared between the two-legal transmitter and receiver. Because of the randomness of local environment and the uniqueness of fiber channel, the phase fluctuation between orthogonal polarization modes (OPMs) can be used as secure keys to enhance the level of security in physical layer. Experimentally, we realize the random key generation and distribution over 25-km standard single-mode fiber (SSMF). Moreover, the proposed key generation scheme has the advantages of low cost, compatible with current optical fiber networks and long distance transmission with optical amplifiers.

CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.

In cloud storage, remote data integrity checking is considered as a crucial technique about data owners who upload enormous data to cloud server provider. A majority of the existing remote data integrity checking protocols rely on the expensive public key infrastructure. In addition, the verification of certificates needs heavy computation and communication cost. Meanwhile, the existing some protocols are not secure under the quantum computer attacks. However, lattice-based constructed cryptography can resist quantum computer attacks and is fairly effective, involving matrix-matrix or matrix-vector multiplications. So, we propose an identity-based remote data integrity checking protocol from lattices, which can eliminate the certificate management process and resist quantum computer attacks. Our protocol is completeness and provably secure based on the hardness small integer solution assumption. The presented scheme is secure against cloud service provider attacks, and leaks no any blocks of the stored file to the third party auditor during verification stage, namely the data privacy against the curiosity third party auditor attacks. The cloud service provider attack includes lost attack and tamper attack. Furthermore, the performance analysis of some protocols demonstrate that our protocol of remote data integrity checking is useful and efficient.

In this paper, we address the problem of demand response of electrical vehicles (EVs) during microgrid outages in the smart grid through the application of Vehicle-to-Grid (V2G) technology. Particularly, we present a novel privacy-preserving double auction scheme. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, solving the social welfare maximization problem of matching buyers to sellers, and the cloud is used as a broker between bidders and the auctioneer, protecting privacy through homomorphic encryption. Theoretical analysis is conducted to validate our auction scheme in satisfying the intended economic and privacy properties (e.g., strategy-proofness and k-anonymity). We also evaluate the performance of the proposed scheme to confirm its practical effectiveness.

The previous consideration of power grid focuses on the power system itself, however, the recent work is aiming at both power grid and communication network, this coupling networks are firstly called as interdependent networks. Prior study on modeling interdependent networks always extracts main features from real networks, the model of network A and network B are completely symmetrical, both degree distribution in intranetwork and support pattern in inter-network, but in reality this circumstance is hard to attain. In this paper, we deliberately set both networks with same topology in order to specialized research the support pattern between networks. In terms of initial failure from power grid or communication network, we find the remaining survival fraction is greatly disparate, and the failure initially from power grid is more harmful than failure initially from communication network, which all show the vulnerability of interdependency and meantime guide us to pay more attention to the protection measures for power grid.