This paper proposes a multiple-image encryption (MIE) approach that uses a novel exponent–sine–cosine (ESC) chaotic map along with the dynamic permutation and DNA-based diffusion. In the first phase ...of the proposed approach, the three components of a color image (‘R’, ‘G’, ‘B’) for all given images are split, cross-shuffled, and combined randomly to create three big images. These resultant three images are permuted using dynamic permutation during the second phase. In the third phase, the permutated image is diffused using the DNA-based diffusion process. Both permutation and diffusion phases use the novel proposed ESC chaotic map. The proposed ESC chaotic map has been analyzed using Shannon entropy, Lyapunov exponent, and bifurcation diagram. The results show that the ESC map is chaotic in the range of 1.5–10. In addition, the proposed MIE algorithm has been evaluated using various standard metrics such as number of pixel change rate (NPCR), unified average change intensity (UACI), entropy, brute force attack, key sensitivity, and bit corrected ratio (BCR). The results show that the value of the NPCR, UACI, and entropy lies close to 99.59, 32.9, and 7.9995, respectively. Therefore, it is validated that the proposed algorithm provides a good encryption mechanism.
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Image encryption converts the images into unrecognizable forms that seems like a white noise. Digital Chaos has also emerged as one of the important technique to design secure and efficient image ...encryption schemes. The chaos theory possesses various desirable properties required for the encrypting images like initial state sensitivity, unpredictability and behavioural complexity. However, image encryption schemes suffer from vulnerabilities like differential attack, statistical, known/chosen plaintext attack, and brute force attack. This paper proposes a novel approach to generate a pseudorandom key. This pseudo random key is combined with Logistic map (LM), cosine transformed Logistic map (CTLM) and cosine transformed Logistic-Sine Map (CTLSM), one by one, to implement three secure and efficient methods for satellite image encryption. The scheme uses the 384-bit of share key to perform encryption during the process. The proposed approaches are tested for parameters such as Entropy, Correlation Coefficient (CC), Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Avalanche effect, Bit Correct Ratio (BCR) and Peak Signal to Noise Ratio (PSNR). The work also analyses the various cryptanalytic attacks on the proposed chaos and novel Pseudo random key combinations. The results show that the proposed pseudo random key and CTLSM combination outperforms the other two combinations, and is more efficient in resisting all type of attacks as well.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Statistics have shown that many accidents occur due to drowsy condition of drivers. In a study conducted by National Sleep Foundation, it has been found that about 20% of drivers feel drowsy during ...driving. These statistics paint a very scary picture. This paper proposes a system for driver drowsiness detection, in which the architecture detects sleepiness of driver. The proposed architecture consists of four deep learning models: AlexNet, VGG-FaceNet, FlowImageNet and ResNet, which use RGB videos of drivers as input and help in detecting drowsiness. Also, these models consider four types of different features such as hand gestures, facial expressions, behavioral features and head movements for the implementation. The AlexNet model is used for various background and environmental changes like indoor, outdoor, day and night. VGG-FaceNet is used to extract facial characteristics like gender ethnicities. FlowImageNet is used for behavioral features and head gestures, and ResNet is used for hand gestures. Hand gestures detection provides a precise and accurate result. These models classify these features into four classes: non-drowsiness, drowsiness with eye blinking, yawning and nodding. The output of these models is provided to ensemble algorithm to obtain a final output by putting them through a SoftMax classifier that gives us a positive (drowsy) or negative answer. The accuracy obtained from this system came out to be 85%.
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Category:
Other; Basic Sciences/Biologics
Introduction/Purpose:
Congenital Talipes Equino-Varus is amongst the most common congenital orthopaedic anomalies. Limb length discrepancy is considered as ...the commonest complication in patients with unilateral clubfoot despite the best non- operative correction. In the present series of cases we aim to study all the possible effects on the lower limb anthropometry in patients with unilateral idiopathic clubfoot who have been managed conservatively.
Methods:
This retrospective study included 47 patients with idiopathic, unilateral CTEV who were successfully treated with Ponseti’s technique. Patients with neuromuscular causes, those requiring extensive soft tissue releases or osteotomies and cases with relapsed or recurrent CTEV were excluded. Casting was initiated following maternal manipulation for one to two weeks. Pirani scores were recorded initially and weekly till correction. After deformity correction, anthropometric measurements (femur and tibia length, calf girth and foot length and foot height) were recorded and compared to the opposite normal limb.
Results:
Only four patients showed a difference in their femur length while difference in tibia length was noted in 10 patients which were statistically not significant. A significant difference was found in the calf girth, foot height and foot length affecting 28, 43 and 42 patients respectively.
Conclusion:
Patients with unilateral clubfoot treated with Ponseti’s correction tend to have anthropometric difference between the affected and the unaffected extremity. While there is a relative sparing of femur and tibia length, calf girth, foot height and foot length are consistently less compared to the unaffected side.
Automatic speaker verification (ASV) systems are enhanced enough, that industry is attracted to use them practically in security systems. However, vulnerability of these systems to various direct and ...indirect access attacks weakens the power of ASV authentication mechanism. The increasing research in spoofing and anti-spoofing technologies is contributing to the enhancement of these systems. The objective of this paper is to review and analyze these important advancements proposed by different researchers and scientists. Various classical, autoregressive, cepstral, etc., and modern deep learning based feature extraction techniques that are chosen to design the frontend of these systems are discussed. Extracted features are learned and classified in the backend of an ASV system, which can be classical machine learning or deep learning models that are also the main focus of the presented review. Experimental studies use constantly modified datasets and evaluation measures to develop robust systems since emergence of practical work in this area. This paper analysis most of the contributing spoofed speech datasets and evaluation protocols. Speech synthesis (SS), voice conversion (VC), replay, mimicry and twins are the potential spoofing attacks to ASV systems. This work provides the knowledge of generation techniques of these attacks to empower the defence mechanism of ASV. This survey marks the start of a new era in ASV system development and highlights the start of a new generation (G
4
) in SS attack development methods. With the increase in advancement of deep learning techniques, the paper makes best efforts to give the complete idea of ASV to new comers to this area and also, puts some light on some of the spoofing attacks that can be targeted during implementation of the future ASV systems.
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Automatic Speech Recognition (ASR) system is an emerging technology used in various fields such as robotics, traffic controls, and healthcare, etc. The leading cause of ASR performance degradation is ...mismatch between the training and testing environments. The main reason for this mismatch is the presence of noise during the testing phase of an ASR system. Various techniques have been used by different researchers in front and backend phases of ASR, to detect and handle the noise. However, a very few review papers have considered noise as a criterion to present the comparison among the existing research works. Hence, the objective of this survey is to analyze and review all the effective methods proposed by different scientists and researchers to boost the noise robustness of an ASR system. Initially, the paper discusses the basic architecture of an ASR system, the factors affecting the its performance, and noise problem formulation. Secondly, the work analysis existing state of the art noise robust ASR methods in terms of front end feature extraction techniques and backend classification model. Then, a detailed review in terms of various speech databases, that are used by these methods, is given. Finally, an analysis in terms of performance metrics of all these noise-resistant ASR techniques is presented. Also, the paper discusses various feature extraction techniques, backend classification methods, different speech databases and performance metrics in detail, while presenting the analysis. The paper also discusses the existing challenges, and describes future research directions in the area of building noise-resistant ASR systems.
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With the increasing adoption of voice-based authentication systems, the threat of audio spoofing attacks has become a significant concern. These attacks aim to deceive voice authentication systems by ...manipulating or impersonating audio signals. To improve the audios security, we have introduced a spectrogram-based solution. Spectrograms, known for their effectiveness in audio analysis and feature extraction, offer valuable insights into combating audio spoofing. Our proposed model is divided into two parts that is frontend and backend. For implementing the frontend, our proposed model extensively investigates the utility of Mel Spectrogram, Gammatone Cepstral Coefficients Spectrogram (GTCC), Acoustic Ternary Pattern Spectrogram (ATP), and Mel-Frequency Cepstral Coefficients Spectrogram (MFCC). For backend implementation, two deep learning models that are Convolutional Neural Network (CNN) and Residual Network (ResNet50) have been leveraged individually with these four spectrograms. The effectiveness of the proposed system is validated through successful experimentation on the ASV Spoof 2019 Logical Access (LA), Physical Access (PA) evaluation datasets and our own Voice Impersonation Corpus in Hindi Language (VIHL) dataset. The outcome demonstrates that the proposed combination of GTCC spectrograms and ResNet50 outperforms all other proposed combinations by achieving Equal Error Rate (EER) of 0.6%, 1.15%, 4.3% for LA, PA and VIHL, respectively.
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•The work proposed in this paper combines trigonometric transformations sine and tangent to proposes a novel one dimensional chaotic map that has been named as one-dimensional sine-tangent chaotic ...(STC) map.•The proposed novel one-dimensional STC map has been combined with a shared key to propose a permutation based medical image encryption scheme.•The performance of a novel STC map has been evaluated using different performance metrics such as bifurcation diagram, lyapunov exponent (LE) and shannon entropy (SE). The comparative results with existing chaotic maps reveal that the proposed novel STC map is more complex, has wider chaotic range and more random in nature.•The proposed medical image encryption system has been evaluated against various testing parameters such as CC, NPCR, UACI, avalanche effect, BCR, and peak PSNR etc. The observed results show that the proposed cryptosystem successfully passes all these tests.
Medical images play a crucial role in the diagnostic, planning, and monitoring of various types of diseases and physical problems. These images contain confidential details about the patient's condition. This confidential data can be safeguarded by using techniques like image encryption that transforms a given image into an unidentifiable format. This research paper proposes a medical image cryptosystem that uses a one-dimensional (1-D) novel Sine-Tangent Chaotic (STC) map and a shared key. Firstly, a private shared key is generated using the XOR operations, on which permutation and substitution operations are carried out. Secondly, a chaotic sequence is generated using the novel STC map. Finally, the permuted-substituted private shared key, the chaotic sequence, and one of the plain image components (i.e. Red, blue or Green) are XORed to construct the cipher image. To test the randomness of the proposed novel 1-D STC, we have used bifurcation diagram, Lyapunov Exponent (LE) and Shannon entropy (SE) parameters. The results show that the proposed mathematical chaotic equation has a wider chaotic range, and is more complex than some of the existing chaotic maps. Also, the observations prove that the proposed medical image encryption method performs well in comparison to the existing state of the art approaches, by obtaining values 7.99977, 99.66023 and 33.80065 for parameters Entropy, Number of Changing Pixel Rates (NPCR) and Unified Averaged Changed Intensity (UACI), respectively.
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The surge in online activities has led to the increasing popularity of sharing video data across diverse applications, including online education tutorials, social networking, video calling, and OTT ...platforms. Encryption prevents unauthorized access to the transmitted data over unreliable channels. The well-known features of chaos theory such as random behaviour, unpredictability, and initial parameters dependency facilitate its use in cryptography. Many security issues are faced by chaos-based cryptosystems because of their less complexity. Hence, a new Cosine-Cosine chaotic map characterized by intricate chaotic behaviour is designed in the current study. Additionally, we formulate an original video encryption scheme employing this Cosine-Cosine chaotic map. The encryption process involves five steps, beginning with the segmentation of the original video into frames based on its frame rate. In the second phase, a 384 bits pseudorandom key is generated that is further divided into three subkeys of 128 bits each. The novel Cosine-Cosine chaotic map-based sequence is generated. In the fourth step, red, green, and blue components are encrypted using the pseudorandom key and the chaotic sequence. In the last step, we combine encrypted frames to get cipher video. The security analysis validates that the proposed encryption protects against eavesdropping.
Automatic Speech Recognition systems that convert language into written text have greatly transformed human–machine interaction. Although these systems have achieved results, in languages building ...accurate and reliable ASR models for low resource languages like Gujarati comes with significant challenges. Gujarati lacks data and linguistic resources, making developing high-performance ASR systems quite difficult. In this paper, we propose an approach to enhance the effectiveness of a Gujarati ASR model despite resources. We achieve this by incorporating integrated features such as Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) utilizing the DeepSpeech2 architecture and implementing an improved spell correction technique based on the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Our approach has demonstrated superiority over previous state-of-the-art methodologies through testing and evaluation. The experimental results demonstrate that our proposed method consistently reduces the Word Error Rate (WER) by 10–12 percentage points compared to the existing work, surpassing the most significant improvement of 5.87%. Our findings demonstrate the viability of developing accurate and dependable ASR systems for languages with limited resources, such as Gujarati.
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