Reviewerly/exHarmony huggingface dataset:
Live and low energy VVC Video Decoding powered by the OpenVVC Decoder on ARM Platform

This demonstration showcases the potential of open-source software implementation for the new versatile video coding (VVC) standard, OpenVVC. The most complex VVC tools were optimized for ARM-type architectures using data parallelism through SIMD instructions. The demonstration has been tested on the NVIDIA Jetson AGX Xavier and on a NVIDIA SHIELD Android TV which showcased real-time decoding for FHD and HD video resolutions. By combining extensive data level parallelism with frame level parallelism, OpenVVC is able to maintain a remarkably low memory and energy consumption while achieving real-time decoding of videos with high resolution. These features present a great advantage for its integration on embedded devices with low computing and memory resources.
An Annealing Processor based on 1k-Spin Fully-Connected Ising Model for Combinatorial Optimization Problems

Combinatorial optimization problems (COPs) find extensive applications in industrial and social scenarios such as transportation and communication. As the size of NP-hard COPs increases, it becomes impossible to obtain the optimal solution using an enumerative method. Recently, Ising model based annealing processors have received increasing attention due to their potential for rapidly converging to the near-optimal solutions after mapping the problem to them. This paper presents a novel annealing processor (AP) with 1024 fully-connected spins based on a modified Ising model annealing algorithm, which is more suitable for hardware implementation compared to conventional simulated annealing (SA) algorithm. The prototype is implemented using FPGA with the operation frequency up to 100MHz. We tested our design on various G-set problems with an average cut accuracy of 99.19% achieved. The proposed design outperforms the conventional CPU-based method by achieving a max speedup of 2204x for G51.
Live Demonstration: Performance Comparison Between Ali266 and x265

This is a live demonstration about performance comparison between Ali266, an optimized VVC software encoder implemented by Alibaba [1], and the open source HEVC software encoder x265 [2]. Visitors can play any pair of comparison videos. The video displayed on the screen is divided into left and right parts. The left part is encoded by the open source HEVC software encoder x265, and the right part is encoded by Ali266. Visitors can directly observe the respective bit rate of either side and the bit rate reduction of Ali266 over x265 under the same subjective quality.
Scalable multi-chip quantum architectures enabled by cryogenic hybrid wireless/quantum-coherent network-in-package

The grand challenge of scaling up quantum computers requires a full-stack architectural standpoint. In this position paper, we will present the vision of a new generation of scalable quantum computing architectures featuring distributed quantum cores (Qcores) interconnected via quantum-coherent qubit state transfer links and orchestrated via an integrated wireless interconnect.
Improving the Efficiency of CMOS Image Sensors through In-Sensor Selective Attention

Inspired by the selective attention mechanism in human vision, we propose to introduce a saliency-based processing step in the CMOS image sensor, to continuously select pixels corresponding to salient objects and feedback such information to the sensor, instead of blindly passing all pixels to the sensor output. To minimize the overhead of saliency detection in this feedback loop, we propose two techniques: (1) saliency detection with low-precision, down-sampled grayscale images, and (2) Optimization of the loss function and model structure. Finally, we pad the minimum number of pixels around the selected pixels to maintain the accuracy of object detection (OD). Our method is experimented with two types of OD algorithms on three representative datasets. At the similar OD accuracy with the full image, our proposed selective feedback method successfully achieves 70.5% reduction in the volume of output pixels for BDD100K, which translates to 4.3× and 3.4× reduction in power consumption and latency, respectively.
SaGNN: a Sample-based GNN Training and Inference Hardware Accelerator

Graph neural networks (GNNs) operations contain a large number of irregular data operations and sparse matrix multiplications, resulting in the under-utilization of computing resources. The problem becomes even more complex and challenging when it comes to large graph training. Scaling GNN training is an effective solution. However, the current GNN operation accelerators do not support the mini-batch structure. We analyze the GNN operational characteristics from multiple aspects and take both the acceleration requirements in the GNN training and inference process into account, and then propose the SaGNN system structure. SaGNN offers multiple working modes to provide acceleration solutions for different GNN frameworks while ensuring system configurability and scalability. Compared to related works, SaGNN brings 5.0x improvement in system performance.
The Influence of Text-guidance on Visual Attention

Visual attention analysis and prediction have long been important tasks in computer vision and image processing. However, images often come along with various text descriptions in real applications, while the influence of these text-guidances on the visual saliency of corresponding images have rarely been studied. Therefore, in this paper, we mainly focus on the problem of whether and how the text-guidance influences the visual attention during image viewing, and perform subjective experiments, qualitative and quantitative comparisons as well as model evaluations on this new task. Specifically, we first conduct eye tracking experiments on 300 images under text-visual (TV) and visual (V) test conditions, respectively. Based on the subjective experiments, we perform qualitative and quantitative comparisons between the visual attention data collected under TV and V conditions, and conclude that the text-guidance can significantly influence the visual attention, especially when the text-described target is a non-salient object. Finally, we evaluate the existing saliency models on our database, and find that existing models cannot well handle this text-induced saliency prediction task. Our constructed database will be publicly available to facilitate future research.
AI-Guardian: Defeating Adversarial Attacks using Backdoors

Deep neural networks (DNNs) have been widely used in many fields due to their increasingly high accuracy. However, they are also vulnerable to adversarial attacks, posing a serious threat to security-critical applications such as autonomous driving, remote diagnosis, etc. Existing solutions are limited in detecting/preventing such attacks, and also impacting the performance on the original tasks. In this paper, we present AI-Guardian, a novel approach to defeating adversarial attacks that leverages intentionally embedded backdoors to fail the adversarial perturbations and maintain the performance of the original main task. We extensively evaluate AI-Guardian using five popular adversarial example generation approaches, and experimental results demonstrate its efficacy in defeating adversarial attacks. Specifically, AI-Guardian reduces the attack success rate from 97.3% to 3.2%, which outperforms the state-of-the-art works by 30.9%, with only a 0.9% decline on the clean data accuracy. Furthermore, AI-Guardian introduces only 0.36% overhead to the model prediction time, almost negligible in most cases.
Private Access Control for Function Secret Sharing

Function Secret Sharing (FSS; Eurocrypt 2015) allows a dealer to share a function f with two or more evaluators. Given secret shares of a function f, the evaluators can locally compute secret shares of f (x) for any input x, without learning information about f in the process.In this paper, we initiate the study of access control for FSS. Given the shares of f, the evaluators can ensure that the dealer is authorized to share the provided function. For a function family $\mathcal{F}$ and an access control list defined over the family, the evaluators receiving the shares of $f \in \mathcal{F}$ can efficiently check that the dealer knows the access key for f.This model enables new applications of FSS, such as: (1) anonymous authentication in a multi-party setting, (2) access control in private databases, and (3) authentication and spam prevention in anonymous communication systems.Our definitions and constructions abstract and improve the concrete efficiency of several recent systems that implement ad-hoc mechanisms for access control over FSS. The main building block behind our efficiency improvement is a discrete-logarithm zero-knowledge proof-of-knowledge over secret-shared elements, which may be of independent interest.We evaluate our constructions and show a 50–70× reduction in computational overhead compared to existing access control techniques used in anonymous communication. In other applications, such as private databases, the processing cost of introducing access control is only 1.5–3×, when amortized over databases with 500,000 or more items.
Optimistic Fast Confirmation While Tolerating Malicious Majority in Blockchains

The robustness of a blockchain against the adversary is often characterized by the maximum fraction (f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> ) of adversarial power that it can tolerate. While most existing blockchains can only tolerate ${f_{\max }} < \frac{1}{2}$ or lower, there are some blockchain systems that are able to tolerate a malicious majority, namely ${f_{\max }} \geq \frac{1}{2}$. A key price paid by such blockchains, however, is their large confirmation latency. This work aims to significantly reduce the confirmation latency in such blockchains, under the common case where the actual fraction f of adversarial power is relatively small. To this end, we propose a novel blockchain called Flint. Flint tolerates ${f_{\max }} \geq \frac{1}{2}$ and can give optimistic execution (i.e., fast confirmation) whenever f is relatively small. Our experiments show that the fast confirmation in Flint only takes a few minutes, as compared to several hours of confirmation latency in prior works.
WarpAttack: Bypassing CFI through Compiler-Introduced Double-Fetches

Code-reuse attacks are dangerous threats that attracted the attention of the security community for years. These attacks aim at corrupting important control-flow transfers for taking control of a process without injecting code. Nowadays, the combinations of multiple mitigations (e.g., ASLR, DEP, and CFI) drastically reduced this attack surface, making running code-reuse exploits more challenging.Unfortunately, security mitigations are combined with compiler optimizations, that do not distinguish between security-related and application code. Blindly deploying code optimizations over code-reuse mitigations may undermine their security guarantees. For instance, compilers may introduce double-fetch vulnerabilities that lead to concurrency issues such as Time-Of-Check to Time-Of-Use (TOCTTOU) attacks.In this work, we propose a new attack vector, called WarpAttack, that exploits compiler-introduced double-fetch optimizations to mount TOCTTOU attacks and bypass code-reuse mitigations. We study the mechanism underlying this attack and present a practical proof-of-concept exploit against the last version of Firefox. Additionally, we propose a lightweight analysis to locate vulnerable double-fetch code (with 3% false positives) and conduct research over six popular applications, five operating systems, and four architectures (32 and 64 bits) to study the diffusion of this threat. Moreover, we study the implication of our attack against six CFI implementations. Finally, we investigate possible research lines for addressing this threat and propose practical solutions to be deployed in existing projects.
DepthFake: Spoofing 3D Face Authentication with a 2D Photo

Face authentication has been widely used in access control, and the latest 3D face authentication systems employ 3D liveness detection techniques to cope with the photo replay attacks, whereby an attacker uses a 2D photo to bypass the authentication. In this paper, we analyze the security of 3D liveness detection systems that utilize structured light depth cameras and discover a new attack surface against 3D face authentication systems. We propose DepthFake attacks that can spoof a 3D face authentication using only one single 2D photo. To achieve this goal, DepthFake first estimates the 3D depth information of a target victim’s face from his 2D photo. Then, DepthFake projects the carefully-crafted scatter patterns embedded with the face depth information, in order to empower the 2D photo with 3D authentication properties. We overcome a collection of practical challenges, e.g., depth estimation errors from 2D photos, depth images forgery based on structured light, the alignment of the RGB image and depth images for a face, and implemented DepthFake in laboratory setups. We validated DepthFake on 3 commercial face authentication systems (i.e., Tencent Cloud, Baidu Cloud, and 3DiVi) and one commercial access control device. The results over 50 users demonstrate that DepthFake achieves an overall Depth attack success rate of 79.4% and RGB-D attack success rate of 59.4% in the real world.
WebSpec: Towards Machine-Checked Analysis of Browser Security Mechanisms

The complexity of browsers has steadily increased over the years, driven by the continuous introduction and update of Web platform components, such as novel Web APIs and security mechanisms. Their specifications are manually reviewed by experts to identify potential security issues. However, this process has proved to be error-prone due to the extensiveness of modern browser specifications and the interplay between new and existing Web platform components. To tackle this problem, we developed WebSpec, the first formal security framework for the analysis of browser security mechanisms, which enables both the automatic discovery of logical flaws and the development of machine-checked security proofs. WebSpec, in particular, includes a comprehensive semantic model of the browser in the Coq proof assistant, a formalization in this model of ten Web security invariants, and a toolchain turning the Coq model and the Web invariants into SMT-lib formulas to enable model checking with the Z3 theorem prover. If a violation is found, the toolchain automatically generates executable tests corresponding to the discovered attack trace, which is validated across major browsers.We showcase the effectiveness of WebSpec by discovering two new logical flaws caused by the interaction of different browser mechanisms and by identifying three previously discovered logical flaws in the current Web platform, as well as five in old versions. Finally, we show how WebSpec can aid the verification of our proposed changes to amend the reported inconsistencies affecting the current Web platform.
When Top-down Meets Bottom-up: Detecting and Exploiting Use-After-Cleanup Bugs in Linux Kernel

When a device is detached from the system, Use-After-Cleanup (UAC) bugs can occur because a running kernel thread may be unaware of the device detachment and attempt to use an object that has been released by the cleanup thread. Our investigation suggests that an attacker can exploit the UAC bugs to obtain the capability of arbitrary code execution and privilege escalation, which receives little attention from the community. While existing tools mainly focus on well-known concurrency bugs like data race, few target UAC bugs.In this paper, we propose a tool named UACatcher to systematically detect UAC bugs. UACatcher consists of three main phases. It first scans the entire kernel to find target layers. Next, it adopts the context- and flow-sensitive inter-procedural analysis and the points-to analysis to locate possible free (deallocation) sites in the bottom-up cleanup thread and use (dereference) sites in the top-down kernel thread that can cause UAC bugs. Then, UACatcher uses the routine switch point algorithm which counts on the synchronizations and path constraints to detect UAC bugs among these sites and estimate exploitable ones. For exploitable bugs, we leverage the pseudoterminal-based device emulation technique to develop practical exploits.We have implemented a prototype of UACatcher and evaluated it on 5.11 Linux kernel. As a result, our tool successfully detected 346 UAC bugs, which were reported to the community (277 have been confirmed and fixed and 15 CVEs have been assigned). Additionally, 13 bugs are exploitable, which can be used to develop working exploits that gain the arbitrary code execution primitive in kernel space and achieve the privilege escalation. Finally, we discuss UACatcher’s limitations and propose possible solutions to fix and prevent UAC bugs.
Confident Monte Carlo: Rigorous Analysis of Guessing Curves for Probabilistic Password Models

In password security a defender would like to identify and warn users with weak passwords. Similarly, the defender may also want to predict what fraction of passwords would be cracked within B guesses as the attacker’s guessing budget B varies from small (online attacker) to large (offline attacker). Towards each of these goals the defender would like to quickly estimate the guessing number for each user password pwd assuming that the attacker uses a password cracking model M i.e., how many password guesses will the attacker check before s/he cracks each user password pwd. Since naïve brute-force enumeration can be prohibitively expensive when the guessing number is very large, Dell’Amico and Filippone [1] developed an efficient Monte Carlo algorithm to estimate the guessing number of a given password pwd. While Dell’Amico and Filippone proved that their estimator is unbiased there is no guarantee that the Monte Carlo estimates are accurate nor does the method provide confidence ranges on the estimated guessing number or even indicate if/when there is a higher degree of uncertainty.Our contributions are as follows: First, we identify theoretical examples where, with high probability, Monte Carlo Strength estimation produces highly inaccurate estimates of individual guessing numbers as well as the entire guessing curve. Second, we introduce Confident Monte Carlo Strength Estimation as an extension of Dell’Amico and Filippone [1]. Given a password our estimator generates an upper and lower bound with the guarantee that, except with probability δ, the true guessing number lies within the given confidence range. Our techniques can also be used to characterize the attacker’s guessing curve. In particular, given a probabilistic password cracking model M we can generate high confidence upper and lower bounds on the fraction of passwords that the attacker will crack as the guessing budget B varies.
WeRLman: To Tackle Whale (Transactions), Go Deep (RL)

The security of proof-of-work blockchain protocols critically relies on incentives. Their operators, called miners, receive rewards for creating blocks containing user-generated transactions. Each block rewards its creator with newly minted tokens and with transaction fees paid by the users. The protocol stability is violated if any of the miners surpasses a threshold ratio of the computational power; she is then motivated to deviate with selfish mining and increase her rewards.Previous analyses of selfish mining strategies assumed constant rewards. But with statistics from operational systems, we show that there are occasional whales – blocks with exceptional rewards. Modeling this behavior implies a state-space that grows exponentially with the parameters, becoming prohibitively large for existing analysis tools.We present the WeRLman <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> framework to analyze such models. WeRLman uses deep Reinforcement Learning (RL), inspired by the state-of-the-art AlphaGo Zero algorithm. Directly extending AlphaGo Zero to a stochastic model leads to high sampling noise, which is detrimental to the learning process. Therefore, WeRLman employs novel variance reduction techniques by exploiting the recurrent nature of the system and prior knowledge of transition probabilities. Evaluating WeRLman against models we can accurately solve demonstrates it achieves unprecedented accuracy in deep RL for blockchain.We use WeRLman to analyze the incentives of a rational miner in various settings and upper-bound the security threshold of Bitcoin-like blockchains. We show, for the first time, a negative relationship between fee variability and the security threshold. The previously known bound, with constant rewards, stands at 0.25 [2]. We show that considering whale transactions reduces this threshold considerably. In particular, with Bitcoin historical fees and its future minting policy, its threshold for deviation will drop to 0.2 in 10 years, 0.17 in 20 years, and to 0.12 in 30 years. With recent fees from the Ethereum smart-contract platform, the threshold drops to 0.17. These are below the common sizes of large miners [3].
Only Pay for What You Leak: Leveraging Sandboxes for a Minimally Invasive Browser Fingerprinting Defense

We present Sandcastle, an entropy-based browser fingerprinting defense that aims to minimize its interference with legitimate web applications. Sandcastle allows developers to partition code that operates on identifiable information into sandboxes to prove to the browser the information cannot be sent in any network request. Meanwhile, sandboxes may make full use of identifiable information on the client side, including writing to dedicated regions of the Document Object Model. For applications where this policy is too strict, Sandcastle provides an expressive cashier that allows precise control over the granularity of data that is leaked to the network. These features allow Sandcastle to eliminate most or all of the noise added to the outputs of identifiable APIs by Chrome’s Privacy Budget framework, the current state of the art in entropy-based fingerprinting defenses. Enabling unlimited client-side use of identifiable information allows for a much more comprehensive set of web applications to run under a fingerprinting defense, such as 3D games and video streaming, and provides a mechanism to expand the space of APIs that can be introduced to the web ecosystem without sacrificing privacy.
ImU: Physical Impersonating Attack for Face Recognition System with Natural Style Changes

This paper presents a novel physical impersonating attack against face recognition systems. It aims at generating consistent style changes across multiple pictures of the attacker under different conditions and poses. Additionally, the style changes are required to be physically realizable by make-up and can induce the intended misclassification. To achieve the goal, we develop novel techniques to embed multiple pictures of the same physical person to vectors in the StyleGAN’s latent space, such that the embedded latent vectors have some implicit correlations to make the search for consistent style changes feasible. Our digital and physical evaluation results show our approach can allow an outsider attacker to successfully impersonate the insiders with consistent and natural changes.