Simultaneous Adversarial Attacks On Multiple Face Recognition System Components

In this work, we investigate the potential threat of adversarial examples to the security of face recognition systems. Although previous research has explored the adversarial risk to individual components of FRSs, our study presents an initial exploration of an adversary simultaneously fooling multiple components: the face detector and feature extractor in an FRS pipeline. We propose three multi-objective attacks on FRSs and demonstrate their effectiveness through a preliminary experimental analysis on a target system. Our attacks achieved up to 100% Attack Success Rates against both the face detector and feature extractor and were able to manipulate the face detection probability by up to 50% depending on the adversarial objective. This research identifies and examines novel attack vectors against FRSs and suggests possible ways to augment the robustness by leveraging the attack vector's knowledge during training of an FRS's components

Latent SHAP: Toward Practical Human-Interpretable Explanations

Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural networks. However, since complex classification models produce superior performance when trained on low-level (or encoded) features, in many cases, the explanations generated by these algorithms are neither interpretable nor usable by humans. Methods proposed in recent studies that support the generation of human-interpretable explanations are impractical, because they require a fully invertible transformation function that maps the model's input features to the human-interpretable features. In this work, we introduce Latent SHAP, a black-box feature attribution framework that provides human-interpretable explanations, without the requirement for a fully invertible transformation function. We demonstrate Latent SHAP's effectiveness using (1) a controlled experiment where invertible transformation functions are available, which enables robust quantitative evaluation of our method, and (2) celebrity attractiveness classification (using the CelebA dataset) where invertible transformation functions are not available, which enables thorough qualitative evaluation of our method

High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority

In this paper, we describe a new information-theoretic protocol (and a computationally-secure variant) for secure {\em three}-party computation with an honest majority. The protocol has very minimal computation and communication; for Boolean circuits, each party sends only a single bit for every AND gate (and nothing is sent for XOR gates). Our protocol is (simulation-based) secure in the presence of semi-honest adversaries, and achieves privacy in the client/server model in the presence of malicious adversaries. On a cluster of three 20-core servers with a 10Gbps connection, the implementation of our protocol carries out over \textit{1.3 million} AES computations per second, which involves processing over \textit{7 billion gates per second}. In addition, we developed a Kerberos extension that replaces the ticket-granting-ticket encryption on the Key Distribution Center (KDC) in MIT-Kerberos with our protocol, using keys/ passwords that are shared between the servers. This enables the use of Kerberos while protecting passwords. Our implementation is able to support a login storm of over 35,000 logins per second, which suffices even for very large organizations. Our work demonstrates that high-throughput secure computation is possible on standard hardware

Generalizing the SPDZ Compiler For Other Protocols

Protocols for secure multiparty computation (MPC) enable a set of mutually distrusting parties to compute an arbitrary function of their inputs while preserving basic security properties like \emph{privacy} and \emph{correctness}. The study of MPC was initiated in the 1980s where it was shown that any function can be securely computed, thus demonstrating the power of this notion. However, these proofs of feasibility were theoretical in nature and it is only recently that MPC protocols started to become efficient enough for use in practice. Today, we have protocols that can carry out large and complex computations in very reasonable time (and can even be very fast, depending on the computation and the setting). Despite this amazing progress, there is still a major obstacle to the adoption and use of MPC due to the huge expertise needed to design a specific MPC execution. In particular, the function to be computed needs to be represented as an appropriate Boolean or arithmetic circuit, and this requires very specific expertise. In order to overcome this, there has been considerable work on compilation of code to (typically) Boolean circuits. One work in this direction takes a different approach, and this is the SPDZ compiler (not to be confused with the SPDZ protocol) that takes high-level Python code and provides an MPC run-time environment for securely executing that code. The SPDZ compiler can deal with arithmetic and non-arithmetic operations and is extremely powerful. However, until now, the SPDZ compiler could only be used for the specific SPDZ family of protocols, making its general applicability and usefulness very limited. In this paper, we extend the SPDZ compiler so that it can work with general underlying protocols. Our SPDZ extensions were made in mind to enable the use of SPDZ for arbitrary protocols and to make it easy for others to integrate existing and new protocols. We integrated three different types of protocols, an honest-majority protocol for computing arithmetic circuits over a field (for any number of parties), a three-party honest majority protocol for computing arithmetic circuits over the ring of integers $\Z_{2^n}$, and the multiparty BMR protocol for computing Boolean circuits. We show that a single high-level SPDZ-Python program can be executed using all of these underlying protocols (as well as the original SPDZ protocol), thereby making SPDZ a true general run-time MPC environment. In order to be able to handle both arithmetic and non-arithmetic operations, the SPDZ compiler relies on conversions from field elements to bits and back. However, these conversions do not apply to ring elements (in particular, they require element division), and we therefore introduce new bit decomposition and recomposition protocols for the ring over integers with replicated secret sharing. These conversions are of independent interest and utilize the structure of $\Z_{2^n}$ (which is much more amenable to bit decomposition than prime-order fields), and are thus much more efficient than all previous methods. We demonstrate our compiler extensions by running a complex SQL query and a decision tree evaluation over all protocols

Conclave: secure multi-party computation on big data (extended TR)

Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data. Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use. Many relational analytics queries can maintain MPC's end-to-end security guarantee without using cryptographic MPC techniques for all operations. Conclave is a query compiler that accelerates such queries by transforming them into a combination of data-parallel, local cleartext processing and small MPC steps. When parties trust others with specific subsets of the data, Conclave applies new hybrid MPC-cleartext protocols to run additional steps outside of MPC and improve scalability further. Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave scales to data sets between three and six orders of magnitude larger than state-of-the-art MPC frameworks support on their own. Thanks to its hybrid protocols, Conclave also substantially outperforms SMCQL, the most similar existing system.Comment: Extended technical report for EuroSys 2019 pape

Report on Teacher training project at the Faculty of Physical Education, Kokushikan University

J-GLOBAL ID : 202101020656121305J-GLOBAL ID : 201401069758702586J-GLOBAL ID : 201801009870115151J-GLOBAL ID : 201501093312486680J-GLOBAL ID : 201901009095161858J-GLOBAL ID : 200901014770791536J-GLOBAL ID : 2009010173422467431520578603916184320application/pdfdepartmental bulletin pape

Insights into Land Plant Evolution Garnered from the Marchantia polymorpha Genome.

The evolution of land flora transformed the terrestrial environment. Land plants evolved from an ancestral charophycean alga from which they inherited developmental, biochemical, and cell biological attributes. Additional biochemical and physiological adaptations to land, and a life cycle with an alternation between multicellular haploid and diploid generations that facilitated efficient dispersal of desiccation tolerant spores, evolved in the ancestral land plant. We analyzed the genome of the liverwort Marchantia polymorpha, a member of a basal land plant lineage. Relative to charophycean algae, land plant genomes are characterized by genes encoding novel biochemical pathways, new phytohormone signaling pathways (notably auxin), expanded repertoires of signaling pathways, and increased diversity in some transcription factor families. Compared with other sequenced land plants, M. polymorpha exhibits low genetic redundancy in most regulatory pathways, with this portion of its genome resembling that predicted for the ancestral land plant. PAPERCLIP