Joint Protection Scheme for Deep Neural Network Hardware Accelerators and Models

Deep neural networks (DNNs) are utilized in numerous image processing, object detection, and video analysis tasks and need to be implemented using hardware accelerators to achieve practical speed. Logic locking is one of the most popular methods for preventing chip counterfeiting. Nevertheless, existing logic-locking schemes need to sacrifice the number of input patterns leading to wrong output under incorrect keys to resist the powerful satisfiability (SAT)-attack. Furthermore, DNN model inference is fault-tolerant. Hence, using a wrong key for those SAT-resistant logic-locking schemes may not affect the accuracy of DNNs. This makes the previous SAT-resistant logic-locking scheme ineffective on protecting DNN accelerators. Besides, to prevent DNN models from being illegally used, the models need to be obfuscated by the designers before they are provided to end-users. Previous obfuscation methods either require long time to retrain the model or leak information about the model. This paper proposes a joint protection scheme for DNN hardware accelerators and models. The DNN accelerator is modified using a hardware key (Hkey) and a model key (Mkey). Different from previous logic locking, the Hkey, which is used to protect the accelerator, does not affect the output when it is wrong. As a result, the SAT attack can be effectively resisted. On the other hand, a wrong Hkey leads to substantial increase in memory accesses, inference time, and energy consumption and makes the accelerator unusable. A correct Mkey can recover the DNN model that is obfuscated by the proposed method. Compared to previous model obfuscation schemes, our proposed method avoids model retraining and does not leak model information

Algorithmic Obfuscation for LDPC Decoders

In order to protect intellectual property against untrusted foundry, many logic-locking schemes have been developed. The main idea of logic locking is to insert a key-controlled block into a circuit to make the circuit function incorrectly without right keys. However, in the case that the algorithm implemented by the circuit is naturally fault-tolerant or self-correcting, existing logic-locking schemes do not affect the system performance much even if wrong keys are used. One example is low-density parity-check (LDPC) error-correcting decoder, which has broad applications in digital communications and storage. This paper proposes two algorithmic-level obfuscation methods for LDPC decoders. By modifying the decoding process and locking the stopping criterion, our new designs substantially degrade the decoder throughput and/or error-correcting performance when the wrong key is used. Besides, our designs are also resistant to the SAT, AppSAT and removal attacks. For an example LDPC decoder, our proposed methods reduce the throughput to less than 1/3 and/or increase the decoder error rate by at least two orders of magnitude with only 0.33% area overhead

On the implementation of modified fuzzy vault for biometric encryption

Abstract—Biometrics, such as irises and fingerprints, enable se-cure and non-repudiable authentication. Fuzzy vault is a scheme that can monolithically bind secret to biometric templates. Moreover, the modified fuzzy vault (MFV) leads to less entropy loss and requires less memory for storing the sketches. This paper proposes a novel low-complexity scheme to compute the monic polynomial for the sketch during the enrollment process of the MFV. An innovative interpolation method is also developed to reduce the computation complexity and latency of the verification process. Efficient hardware implementation architectures are developed in this paper for the proposed schemes and their complexities are analyzed in detail. I

Speed Characteristics and Safety Risk Level Evaluation for Nighttime Roadway Work Area

AbstractMore and more roadway projects are conducted during nighttime to satisfy heavy travelling demands in the daytime. However, the Nighttime Roadway Work (NRW) produces several safety problems. As a developing region of China, these problems expose later than some developed countries, and there is scarcely any valid crash samples for NRW safety analysis. Therefore, a surrogate safety assessment philosophy is adopted in the paper. Firstly, the field survey and statistical analysis approaches are adopted to acquire percentile speed characteristics of vehicles passing through work-zones during nighttime. Secondly, a kind of safety risk evaluation model is established to assess NRW safety levels. The results reveal that 1) speeding problems severely exist in the NRW zone and safety risk levels are comparatively high; 2) from speed characteristics of NRW in a city expressway by lanes, speeding ratio in the activity area is higher than 90% and in the lane in transition area the ratio is higher than 30%; 3) from speed characteristics of NRW in a city expressway by vehicle types, speeding ratios of all type vehicles in the activity and transition area are higher than 90% and 30% respectively; 4) the speeding ratio in freeway NRW area is close to 100%; 5) the safety risk analysis indicates that risk levels of lanes in transition area are comparatively high, and also the safety risk level of large vehicles is higher compared to other type vehicles. Finally, several useful tips for the prevention of speeding in the NRW area are suggested

Climbing the Social Ladder: Does Intergenerational Solidarity matter?

Research on intergenerational transmission of inequality tends to focus on unequal access to wealth as well as human and social capital. Often lost in these discussions is the role of parent-offspring relationships. This study takes a closer look into families and investigates how the heterogeneity in family relationships may affect individual social mobility. We apply the concept of intergenerational solidarity to analyse how family relationships vary in nature. We explore two prominent features - emotional closeness and family obligations. Using World Value Survey microdata from 55 countries, we find that emotional closeness between parents and offspring is positively related to both the possibility and extent of upward occupational mobility. On the other hand, the strength of obligations felt towards family members is negatively associated with upward mobility. The obligations of caring for parents may influence offspring decision making, often hindering opportunities to climb the social ladder

GPR35 acts a dual role and therapeutic target in inflammation

GPR35 is a G protein-coupled receptor with notable involvement in modulating inflammatory responses. Although the precise role of GPR35 in inflammation is not yet fully understood, studies have suggested that it may have both pro- and anti-inflammatory effects depending on the specific cellular environment. Some studies have shown that GPR35 activation can stimulate the production of pro-inflammatory cytokines and facilitate the movement of immune cells towards inflammatory tissues or infected areas. Conversely, other investigations have suggested that GPR35 may possess anti-inflammatory properties in the gastrointestinal tract, liver and certain other tissues by curbing the generation of inflammatory mediators and endorsing the differentiation of regulatory T cells. The intricate role of GPR35 in inflammation underscores the requirement for more in-depth research to thoroughly comprehend its functional mechanisms and its potential significance as a therapeutic target for inflammatory diseases. The purpose of this review is to concurrently investigate the pro-inflammatory and anti-inflammatory roles of GPR35, thus illuminating both facets of this complex issue

Leading and following: Noise differently affects semantic and acoustic processing during naturalistic speech comprehension

Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audio recordings mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, -3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latencies were more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latencies became more leading with increasing noise and decreasing speech comprehension and intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments