Amna Shifa | Bahria University, Islamabad-Pakistan (original) (raw)

Papers by Amna Shifa

IEEE Access, 2020

Video Surveillance (VS) systems are commonly deployed for real-time abnormal event detection and ... more Video Surveillance (VS) systems are commonly deployed for real-time abnormal event detection and autonomous video analytics. Video captured by surveillance cameras in real-time often contains identifiable personal information, which must be privacy protected, sometimes along with the locations of the surveillance and other sensitive information. Within the Surveillance System, these videos are processed and stored on a variety of devices. The processing and storage heterogeneity of those devices, together with their network requirements, make real-time surveillance systems complex and challenging. This paper proposes a surveillance system, named as Multi-Level Video Security (MuLViS) for privacy-protected cameras. Firstly, a Smart Surveillance Security Ontology (SSSO) is integrated within the MuLViS, with the aim of autonomously selecting the privacy level matching the operating device's hardware specifications and network capabilities. Overall, along with its device-specific security, the system leads to relatively fast indexing and retrieval of surveillance video. Secondly, information within the videos are protected at the times of capturing, streaming, and storage by means of differing encryption levels. An extensive evaluation of the system, through visual inspection and statistical analysis of experimental video results, such as by the Encryption Space Ratio (ESR), has demonstrated the aptness of the security level assignments. The system is suitable for surveillance footage protection, which can be made General Data Protection Regulation (GDPR) compliant, ensuring that lawful data access respects individuals' privacy rights.

Sensors, 2019

Within an Internet of Multimedia Things, the risk of disclosing streamed video content, such as t... more Within an Internet of Multimedia Things, the risk of disclosing streamed video content, such as that arising from video surveillance, is of heightened concern. This leads to the encryption of that content. To reduce the overhead and the lack of flexibility arising from full encryption of the content, a good number of selective-encryption algorithms have been proposed in the last decade. Some of them have limitations, in terms of: significant delay due to computational cost, or excess memory utilization, or, despite being energy efficient, not providing a satisfactory level of confidentiality, due to their simplicity. To address such limitations, this paper presents a lightweight selective encryption scheme, in which encoder syntax elements are encrypted with the innovative EXPer (extended permutation with exclusive OR). The selected syntax elements are taken from the final stage of video encoding that is during the entropy coding stage. As a diagnostic tool, the Encryption Space Ratio measures encoding complexity of the video relative to the level of encryption so as to judge the success of the encryption process, according to entropy coder. A detailed comparative analysis of EXPer with other state-of-the-art encryption algorithms confirms that EXPer provides significant confidentiality with a small computational cost and a negligible encryption bitrate overhead. Thus, the results demonstrate that the proposed security scheme is a suitable choice for constrained devices in an Internet of Multimedia Things environment.

Image and Vision Computing, 2019

An individual's privacy is a significant concern in surveillance videos. Existing research work i... more An individual's privacy is a significant concern in surveillance videos. Existing research work into the location of individuals on the basis of detecting their skin is focused either on different techniques for detecting human skin on protecting individuals from the consequences of applying such techniques. This paper considers both lines of research and proposes a hybrid scheme for human skin detection and subsequent privacy protection by utilizing color information in dynamically varying illumination and environmental conditions. For those purposes, dynamic and explicit skin-detection approaches are implemented, simultaneously considering multiple color-spaces, i.e. RGB, perceptual (HSV) and orthogonal (YCbCr) color-spaces, and then detecting the human skin by the proposed Combined Threshold Rule (CTR)-based segmentation. Comparative qualitative and quantitative detection results with an average 93.73% accuracy, imply that the proposed scheme achieves considerable accuracy without incurring a training cost. Once skin detection has been performed, the detected skin pixels (including false positives) are encrypted, when standard AES-CFB encryption of skin pixels is shown to be preferable compared to selective encryption of a whole video frame. The scheme preserves the behavior of the subjects within the video. Hence, subsequent image processing and behavior analysis, if required, can be performed by an authorized user. The experimental results are encouraging, as they show that the average encryption time is 8.268 s and the Encryption Space Ratio (ESR) is an average 7.25% for a high definition video (1280 × 720 pixels/frame). A performance comparison in terms of Correct Detection Rate (CDR) showed an average 91.5% for CTB-based segmentation compared to using only one color space for segmentation, such as using RGB with 85.86%, HSV with 80.93% and YCbCr with an average 84.8%, which implies that the proposed method of combining color-space skin identifications has a higher ability to detect skin accurately. Security analysis confirmed that the proposed scheme could be a suitable choice for real-time surveillance applications operating on resource-constrained devices.

— The Internet of Things (IoT) is an emerging paradigm in which physical objects are connected to... more — The Internet of Things (IoT) is an emerging paradigm in which physical objects are connected to each other and user via the Internet in order to share information between devices and systems. In the IoT environment, smart devices are deployed to monitor, control and analyze business, personal, and social activities. The pervasive nature of the IoT may well be beneficial but it also presents a risk from malware, hacker intrusion, viruses and the like. The consequent security and privacy issues may cause physical damage and even threaten human lives. This paper targets two areas; initially,the paper provides a review of the IoT layered architecture with the security challenges/attacks to setting up an IoT environment. After that, the paper proposes a solution which can preserve the privacy of multimedia data within anIoT environment atits perception layer. The proposed security solutionwill support the multimedia to function effectively, while at the same time preserving the confidentiality of the information transmitted and the privacy of individuals.

Owing to the exceptional growth of information exchange over open communication channels within t... more Owing to the exceptional growth of information exchange over open communication channels within the public Internet, confidential transmission of information has become a vital current concern for organizations and individuals. In the proposed content-protection scheme, the decryption key is embedded in the encrypted image by utilizing machine learning, nearest-centroid clustering classifier, followed by least significant bit matching (LSB-M) in the spatial domain. An image is first encrypted with the advanced encryption standard (AES) algorithm in output feedback mode, after which the AES key is embedded into the encrypted image. Preliminary nearest-centroid clustering followed by shuffling the sequence of pixels within the clusters before applying LSB-M makes any attack more complex, as the bits of the key are further dispersed within the encrypted image. In terms of contributions, one contribution is the direct implementation of the proposed security mechanism on color images rather than first converting them into gray tones. Another contribution of the Crypto-Stego method is that, it requires no separate key distribution mechanism to decrypt the information. In addition, a parallel-processing approach is implemented to improve the execution time and the efficiency of the scheme by exploiting system resources. Extensive experiments were performed on RGB images with different resolutions and sizes to confirm the effectiveness of the scheme. The high structural similarity index score confirmed that the overall carrier image and stego-image were unaltered by processing. While an average value over the test images of 0.0594 for mean squared error confirmed that malicious individuals cannot detect the presence of stego data in the cover image. Moreover, negligible pixel intensity histogram changes also validated the effectiveness of the proposed scheme. An average 77% efficiency and 1.5 times speed-up factor were achieved through parallel processing showed the effectiveness of the joint Crypto-Stego method for image confidentiality.

IEEE Access, 2020

Video Surveillance (VS) systems are commonly deployed for real-time abnormal event detection and ... more Video Surveillance (VS) systems are commonly deployed for real-time abnormal event detection and autonomous video analytics. Video captured by surveillance cameras in real-time often contains identifiable personal information, which must be privacy protected, sometimes along with the locations of the surveillance and other sensitive information. Within the Surveillance System, these videos are processed and stored on a variety of devices. The processing and storage heterogeneity of those devices, together with their network requirements, make real-time surveillance systems complex and challenging. This paper proposes a surveillance system, named as Multi-Level Video Security (MuLViS) for privacy-protected cameras. Firstly, a Smart Surveillance Security Ontology (SSSO) is integrated within the MuLViS, with the aim of autonomously selecting the privacy level matching the operating device's hardware specifications and network capabilities. Overall, along with its device-specific security, the system leads to relatively fast indexing and retrieval of surveillance video. Secondly, information within the videos are protected at the times of capturing, streaming, and storage by means of differing encryption levels. An extensive evaluation of the system, through visual inspection and statistical analysis of experimental video results, such as by the Encryption Space Ratio (ESR), has demonstrated the aptness of the security level assignments. The system is suitable for surveillance footage protection, which can be made General Data Protection Regulation (GDPR) compliant, ensuring that lawful data access respects individuals' privacy rights.

Sensors, 2019

Within an Internet of Multimedia Things, the risk of disclosing streamed video content, such as t... more Within an Internet of Multimedia Things, the risk of disclosing streamed video content, such as that arising from video surveillance, is of heightened concern. This leads to the encryption of that content. To reduce the overhead and the lack of flexibility arising from full encryption of the content, a good number of selective-encryption algorithms have been proposed in the last decade. Some of them have limitations, in terms of: significant delay due to computational cost, or excess memory utilization, or, despite being energy efficient, not providing a satisfactory level of confidentiality, due to their simplicity. To address such limitations, this paper presents a lightweight selective encryption scheme, in which encoder syntax elements are encrypted with the innovative EXPer (extended permutation with exclusive OR). The selected syntax elements are taken from the final stage of video encoding that is during the entropy coding stage. As a diagnostic tool, the Encryption Space Ratio measures encoding complexity of the video relative to the level of encryption so as to judge the success of the encryption process, according to entropy coder. A detailed comparative analysis of EXPer with other state-of-the-art encryption algorithms confirms that EXPer provides significant confidentiality with a small computational cost and a negligible encryption bitrate overhead. Thus, the results demonstrate that the proposed security scheme is a suitable choice for constrained devices in an Internet of Multimedia Things environment.

Image and Vision Computing, 2019

An individual's privacy is a significant concern in surveillance videos. Existing research work i... more An individual's privacy is a significant concern in surveillance videos. Existing research work into the location of individuals on the basis of detecting their skin is focused either on different techniques for detecting human skin on protecting individuals from the consequences of applying such techniques. This paper considers both lines of research and proposes a hybrid scheme for human skin detection and subsequent privacy protection by utilizing color information in dynamically varying illumination and environmental conditions. For those purposes, dynamic and explicit skin-detection approaches are implemented, simultaneously considering multiple color-spaces, i.e. RGB, perceptual (HSV) and orthogonal (YCbCr) color-spaces, and then detecting the human skin by the proposed Combined Threshold Rule (CTR)-based segmentation. Comparative qualitative and quantitative detection results with an average 93.73% accuracy, imply that the proposed scheme achieves considerable accuracy without incurring a training cost. Once skin detection has been performed, the detected skin pixels (including false positives) are encrypted, when standard AES-CFB encryption of skin pixels is shown to be preferable compared to selective encryption of a whole video frame. The scheme preserves the behavior of the subjects within the video. Hence, subsequent image processing and behavior analysis, if required, can be performed by an authorized user. The experimental results are encouraging, as they show that the average encryption time is 8.268 s and the Encryption Space Ratio (ESR) is an average 7.25% for a high definition video (1280 × 720 pixels/frame). A performance comparison in terms of Correct Detection Rate (CDR) showed an average 91.5% for CTB-based segmentation compared to using only one color space for segmentation, such as using RGB with 85.86%, HSV with 80.93% and YCbCr with an average 84.8%, which implies that the proposed method of combining color-space skin identifications has a higher ability to detect skin accurately. Security analysis confirmed that the proposed scheme could be a suitable choice for real-time surveillance applications operating on resource-constrained devices.

— The Internet of Things (IoT) is an emerging paradigm in which physical objects are connected to... more — The Internet of Things (IoT) is an emerging paradigm in which physical objects are connected to each other and user via the Internet in order to share information between devices and systems. In the IoT environment, smart devices are deployed to monitor, control and analyze business, personal, and social activities. The pervasive nature of the IoT may well be beneficial but it also presents a risk from malware, hacker intrusion, viruses and the like. The consequent security and privacy issues may cause physical damage and even threaten human lives. This paper targets two areas; initially,the paper provides a review of the IoT layered architecture with the security challenges/attacks to setting up an IoT environment. After that, the paper proposes a solution which can preserve the privacy of multimedia data within anIoT environment atits perception layer. The proposed security solutionwill support the multimedia to function effectively, while at the same time preserving the confidentiality of the information transmitted and the privacy of individuals.

Owing to the exceptional growth of information exchange over open communication channels within t... more Owing to the exceptional growth of information exchange over open communication channels within the public Internet, confidential transmission of information has become a vital current concern for organizations and individuals. In the proposed content-protection scheme, the decryption key is embedded in the encrypted image by utilizing machine learning, nearest-centroid clustering classifier, followed by least significant bit matching (LSB-M) in the spatial domain. An image is first encrypted with the advanced encryption standard (AES) algorithm in output feedback mode, after which the AES key is embedded into the encrypted image. Preliminary nearest-centroid clustering followed by shuffling the sequence of pixels within the clusters before applying LSB-M makes any attack more complex, as the bits of the key are further dispersed within the encrypted image. In terms of contributions, one contribution is the direct implementation of the proposed security mechanism on color images rather than first converting them into gray tones. Another contribution of the Crypto-Stego method is that, it requires no separate key distribution mechanism to decrypt the information. In addition, a parallel-processing approach is implemented to improve the execution time and the efficiency of the scheme by exploiting system resources. Extensive experiments were performed on RGB images with different resolutions and sizes to confirm the effectiveness of the scheme. The high structural similarity index score confirmed that the overall carrier image and stego-image were unaltered by processing. While an average value over the test images of 0.0594 for mean squared error confirmed that malicious individuals cannot detect the presence of stego data in the cover image. Moreover, negligible pixel intensity histogram changes also validated the effectiveness of the proposed scheme. An average 77% efficiency and 1.5 times speed-up factor were achieved through parallel processing showed the effectiveness of the joint Crypto-Stego method for image confidentiality.