17 research outputs found
Universality of underlying mechanism for successful deep learning
An underlying mechanism for successful deep learning (DL) with a limited deep
architecture and dataset, namely VGG-16 on CIFAR-10, was recently presented
based on a quantitative method to measure the quality of a single filter in
each layer. In this method, each filter identifies small clusters of possible
output labels, with additional noise selected as labels out of the clusters.
This feature is progressively sharpened with the layers, resulting in an
enhanced signal-to-noise ratio (SNR) and higher accuracy. In this study, the
suggested universal mechanism is verified for VGG-16 and EfficientNet-B0
trained on the CIFAR-100 and ImageNet datasets with the following main results.
First, the accuracy progressively increases with the layers, whereas the noise
per filter typically progressively decreases. Second, for a given deep
architecture, the maximal error rate increases approximately linearly with the
number of output labels. Third, the average filter cluster size and the number
of clusters per filter at the last convolutional layer adjacent to the output
layer are almost independent of the number of dataset labels in the range [3,
1,000], while a high SNR is preserved. The presented DL mechanism suggests
several techniques, such as applying filter's cluster connections (AFCC), to
improve the computational complexity and accuracy of deep architectures and
furthermore pinpoints the simplification of pre-existing structures while
maintaining their accuracies.Comment: 27 pages,5 figures, 6 tables. arXiv admin note: text overlap with
arXiv:2305.1807
Enhancing the success rates by performing pooling decisions adjacent to the output layer
Learning classification tasks of (2^nx2^n) inputs typically consist of \le n
(2x2) max-pooling (MP) operators along the entire feedforward deep
architecture. Here we show, using the CIFAR-10 database, that pooling decisions
adjacent to the last convolutional layer significantly enhance accuracy success
rates (SRs). In particular, average SRs of the advanced VGG with m layers
(A-VGGm) architectures are 0.936, 0.940, 0.954, 0.955, and 0.955 for m=6, 8,
14, 13, and 16, respectively. The results indicate A-VGG8s' SR is superior to
VGG16s', and that the SRs of A-VGG13 and A-VGG16 are equal, and comparable to
that of Wide-ResNet16. In addition, replacing the three fully connected (FC)
layers with one FC layer, A-VGG6 and A-VGG14, or with several linear activation
FC layers, yielded similar SRs. These significantly enhanced SRs stem from
training the most influential input-output routes, in comparison to the
inferior routes selected following multiple MP decisions along the deep
architecture. In addition, SRs are sensitive to the order of the
non-commutative MP and average pooling operators adjacent to the output layer,
varying the number and location of training routes. The results call for the
reexamination of previously proposed deep architectures and their SRs by
utilizing the proposed pooling strategy adjacent to the output layer.Comment: 27 pages, 3 figures, 1 table and Supplementary Informatio
Efficient shallow learning as an alternative to deep learning
The realization of complex classification tasks requires training of deep
learning (DL) architectures consisting of tens or even hundreds of
convolutional and fully connected hidden layers, which is far from the reality
of the human brain. According to the DL rationale, the first convolutional
layer reveals localized patterns in the input and large-scale patterns in the
following layers, until it reliably characterizes a class of inputs. Here, we
demonstrate that with a fixed ratio between the depths of the first and second
convolutional layers, the error rates of the generalized shallow LeNet
architecture, consisting of only five layers, decay as a power law with the
number of filters in the first convolutional layer. The extrapolation of this
power law indicates that the generalized LeNet can achieve small error rates
that were previously obtained for the CIFAR-10 database using DL architectures.
A power law with a similar exponent also characterizes the generalized VGG-16
architecture. However, this results in a significantly increased number of
operations required to achieve a given error rate with respect to LeNet. This
power law phenomenon governs various generalized LeNet and VGG-16
architectures, hinting at its universal behavior and suggesting a quantitative
hierarchical time-space complexity among machine learning architectures.
Additionally, the conservation law along the convolutional layers, which is the
square-root of their size times their depth, is found to asymptotically
minimize error rates. The efficient shallow learning that is demonstrated in
this study calls for further quantitative examination using various databases
and architectures and its accelerated implementation using future dedicated
hardware developments.Comment: 26 pages, 4 figures (improved figures resolution
The mechanism underlying successful deep learning
Deep architectures consist of tens or hundreds of convolutional layers (CLs)
that terminate with a few fully connected (FC) layers and an output layer
representing the possible labels of a complex classification task. According to
the existing deep learning (DL) rationale, the first CL reveals localized
features from the raw data, whereas the subsequent layers progressively extract
higher-level features required for refined classification. This article
presents an efficient three-phase procedure for quantifying the mechanism
underlying successful DL. First, a deep architecture is trained to maximize the
success rate (SR). Next, the weights of the first several CLs are fixed and
only the concatenated new FC layer connected to the output is trained,
resulting in SRs that progress with the layers. Finally, the trained FC weights
are silenced, except for those emerging from a single filter, enabling the
quantification of the functionality of this filter using a correlation matrix
between input labels and averaged output fields, hence a well-defined set of
quantifiable features is obtained. Each filter essentially selects a single
output label independent of the input label, which seems to prevent high SRs;
however, it counterintuitively identifies a small subset of possible output
labels. This feature is an essential part of the underlying DL mechanism and is
progressively sharpened with layers, resulting in enhanced signal-to-noise
ratios and SRs. Quantitatively, this mechanism is exemplified by the VGG-16,
VGG-6, and AVGG-16. The proposed mechanism underlying DL provides an accurate
tool for identifying each filter's quality and is expected to direct additional
procedures to improve the SR, computational complexity, and latency of DL.Comment: 33 pages, 8 figure
Optical Cryptanalysis: Recovering Cryptographic Keys from Power LED Light Fluctuations
Although power LEDs have been integrated in various
devices that perform cryptographic operations for decades, the
cryptanalysis risk they pose has not yet been investigated.
In this paper, we present optical cryptanalysis, a new form
of cryptanalytic side-channel attack, in which secret keys are
extracted by using a photodiode to measure the light emitted
by a device’s power LED and analyzing subtle fluctuations in
the light intensity during cryptographic operations. We analyze
the optical leakage of power LEDs of various consumer
devices and the factors that affect the optical SNR. We then
demonstrate end-to-end optical cryptanalytic attacks against
a range of consumer devices (smartphone, smartcard, and
Raspberry Pi, along with their USB peripherals) and recover
secret keys (RSA, ECDSA, SIKE) from prior and recent
versions of popular cryptographic libraries (GnuPG, Libgcrypt,
PQCrypto-SIDH) from a maximum distance of 25 meter
The Missing Linkage: Building Effective Governance for Joint and Network Evaluation
Research on governance network evaluation has made great strides in determining what effectiveness is and effectiveness for whom , often introducing a set of indicators or objectives as a basis for network evaluation. On this basis, governance theorists developed frameworks and models to increase network effectiveness in relation to specific determinants. The discussion of indicators and determinants of network effectiveness has been followed by innovative methods to analyse and evaluate them. Yet, the linkage between indicators and determinants of effectiveness and methodologies of network evaluation is still unclear as different evaluation methods can be managed and applied in various ways depending on the nature of the network and the programmes to be evaluated. Concrete applications of joint and network evaluations mirror this theoretical gap, where inappropriate evaluation approaches and management practices of such evaluations cause delays, increases in transaction costs, cumbersome governance structures, dissatisfaction of actors with current evaluation practices and unresolved conflicts that hinder engagement between actors for future collaborative performance assessments. In this dissertation we frame this problem as a problem of governance, and aim to generate knowledge concerning ways to improve governance and increase governability, without giving up on the ability to evaluate processes and final results of highly complex programmes and networks. Differentiating between aspects of demand and supply in evaluation systems and conceiving varied levels of dynamics, complexity and diversity as given conditions to which governance should adapt, the research question in this PhD thesis is: How can the governing system in joint and network evaluations be best adapted to the programmes to be evaluated?By integrating the parameters of evaluation, complexity and governance, this dissertation introduces a configurative model based on levels of dynamics, complexity and diversity that provides procedures for building effective governance for joint and network evaluations as the linkage between network effectiveness and the methods used to assess it. Following a retroductive research design, the model is exemplified in five case studies in order to enrich it and examine its validity and practical application. The field of development aid was chosen as an appropriate arena in which to develop and test the model because of the rich practice and experience generated in this field. However, based on governance networks, complexity and evaluation theories, the model is designed to be applied to various fields, all types of networks and diverse evaluation types. As the model conceives of network evaluation as a network within network , it goes even beyond the evaluation process and points to a new direction for exploring network effectiveness as a whole.In addition to its direct practical contribution, this dissertation adds to the theoretical discussion on network governance and effectiveness. It rejects the current assumption that networks characterized by coordination and cooperation should necessarily be assessed differently from those characterized by collaboration, since each network type can be classified as having either high or low levels of complexity. Furthermore, it rejects the traditional dichotomy between participatory, multi-perspective evaluation and conventional performance indicators and the claim that goal attainment methods as ex-ante formulated objectives have no credibility in collaborative performance assessments. Instead, this dissertation shows that performance measurement and management practices can complement participatory process evaluation, and that ex-ante assumptions may be important tools also in complex networks evaluation, depending on the levels of system dynamics. Other main theoretical contributions of this dissertation are a different but practical and innovative method to analyse complexity, a differentiation between the governing systems and the systems to be governed in networks and the revision of various central concepts in organization and network theories. Finally, perhaps the most important contribution of this dissertation is the transdisciplinary approach to governance and evaluation, demonstrating that the two disciplines have much to contribute to and learn from each other.status: publishe
Towards a universal mechanism for successful deep learning
Abstract Recently, the underlying mechanism for successful deep learning (DL) was presented based on a quantitative method that measures the quality of a single filter in each layer of a DL model, particularly VGG-16 trained on CIFAR-10. This method exemplifies that each filter identifies small clusters of possible output labels, with additional noise selected as labels outside the clusters. This feature is progressively sharpened with each layer, resulting in an enhanced signal-to-noise ratio (SNR), which leads to an increase in the accuracy of the DL network. In this study, this mechanism is verified for VGG-16 and EfficientNet-B0 trained on the CIFAR-100 and ImageNet datasets, and the main results are as follows. First, the accuracy and SNR progressively increase with the layers. Second, for a given deep architecture, the maximal error rate increases approximately linearly with the number of output labels. Third, similar trends were obtained for dataset labels in the range [3, 1000], thus supporting the universality of this mechanism. Understanding the performance of a single filter and its dominating features paves the way to highly dilute the deep architecture without affecting its overall accuracy, and this can be achieved by applying the filter’s cluster connections (AFCC)
Video-Based Cryptanalysis: Extracting Cryptographic Keys from Video Footage of a Device’s Power LED
In this paper, we present video-based cryptanalysis,
a new method used to recover secret keys from a device by
analyzing video footage of a device’s power LED. We show that
cryptographic computations performed by the CPU change the
power consumption of the device which affects the brightness of
the device’s power LED. Based on this observation, we show how
attackers can exploit commercial video cameras (e.g., an iPhone
13’s camera or Internet-connected security camera) to recover
secret keys from devices. This is done by obtaining video footage
of a device’s power LED (in which the frame is filled with the
power LED) and exploiting the video camera’s rolling shutter
to increase the sampling rate by three orders of magnitude
from the FPS rate (60 measurements per second) to the rolling
shutter speed (60K measurements per second in the iPhone 13
Pro Max). The frames of the video footage of the device’s power
LED are analyzed in the RGB space, and the associated RGB
values are used to recover the secret key by inducing the power
consumption of the device from the RGB values. We demonstrate
the application of video-based cryptanalysis by performing two
side-channel cryptanalytic timing attacks and recover: (1) a 256-
bit ECDSA key from a smart card by analyzing video footage of
the power LED of a smart card reader via a hijacked Internet-connected security camera located 16 meters away from the smart
card reader, and (2) a 378-bit SIKE key from a Samsung Galaxy
S8 by analyzing video footage of the power LED of Logitech Z120
USB speakers that were connected to the same USB hub (that
was used to charge the Galaxy S8) via an iPhone 13 Pro Max.
Finally, we discuss countermeasures, limitations, and the future
of video-based cryptanalysis in light of the expected improvements
in video cameras’ specifications