In the rapidly evolving digital landscape, the proliferation of online image and video sharing has reached unprecedented levels. This surge has been accompanied by the emergence of deepfake technology, powered by generative adversarial networks and deep learning techniques, enabling the creation of highly realistic fabricated images and videos. Consequently, the widespread dissemination of deepfake content on social media platforms has prompted the global community to seek effective methods for detecting and combating this issue. Recognizing that Artificial Intelligence (AI) has played a significant role in propagating deepfake challenges, researchers and experts believe that AI can also provide viable solutions. In this study, we explore the application of AI for deepfake image detection. Specifically, we focus on three convolutional neural network (CNN) algorithms—VGG16, VGG19, and ResNet—for this purpose. To evaluate the performance of these CNN models, a dataset comprising 1,200 images, including a combination of fake and real images, was utilized. The deepfake images were generated using FaceApp, a prominent tool for creating manipulated visuals. Our findings demonstrate that VGG19 outperforms both VGG16 and ResNet50, achieving an impressive accuracy rate of 98% on the test dataset when small size is small. By harnessing the power of AI, particularly through the application of CNN algorithms, this research makes a significant contribution to the field of deepfake image detection. The results underscore the efficacy of VGG19 in accurately identifying manipulated images, thereby providing valuable insights for the development of robust detection systems to combat the proliferation of deepfakes in today's digital world.

Using different chaotic systems in secure communication, nonlinear control, and many other applications has revealed that these systems have several drawbacks in different aspects. This can cause unfavorable effects to chaos-based applications. Therefore, presenting a chaotic map with complex behaviors is considered important. In this paper, we introduce a new 2D chaotic map, namely, the 2D infinite-collapse-Sine model (2D-ICSM). Various metrics including Lyapunov exponents and bifurcation diagrams are used to demonstrate the complex dynamics and robust hyperchaotic behavior of the 2D-ICSM. Furthermore, the cross-correlation coefficient, phase space diagram, and Sample Entropy algorithm prove that the 2D-ICSM has a high sensitivity to initial values and parameters, extreme complexity performance, and a much larger hyperchaotic range than existing maps. To empirically verify the efficiency and simplicity of the 2D-ICSM in practical applications, we propose a symmetric secure communication system using the 2D-ICSM. Experimental results are presented to demonstrate the validity of the proposed system.

The RSA is one of the current default cryptosystems that provides security with applications such as encryptions and digital signatures. It is important to further study the weak characteristics of the RSA to ensure correct utilisation in order not to be susceptible to adversaries. In this paper, we give detailed analysis on security of the Murru-Saettone variant of the RSA cryptosystem that utilised a cubic Pell $ed-k\left ({p^{2}+p+1}\right )\left ({q^{2}+q+1}\right )=1$ as key equation and $N=pq$ as RSA modulus. We propose some attacks on this variant when the prime difference $|p-q|$ is small. Our first approach is to utilise the continued fractions algorithm to determine the parameter $d$ which enables us to determine the secret $p$ and $q$ . Our second approach considers the Coppersmith's method and lattice basis reduction to factor the modulus $N$ . Our attacks improve recent cryptanalyses on the cubic Pell equation variant of RSA. Furthermore, our attacks prove that under small prime difference scenario, the number of susceptible private exponents for the cubic Pell equation variant of RSA is much larger than the standard RSA.

This paper presents new short decryption exponent attacks on RSA, which successfully leads to the factorization of RSA modulus $N=pq$ in polynomial time. The paper has two parts. In the first part, we report the usage of the small prime difference method of the form $|b^{2}p-a^{2}q|<N^{\gamma }$ where the ratio of $\frac{q}{p}$ is close to $\frac{b^2}{a^2}$ , which yields a bound $d<\frac{\sqrt{3}}{\sqrt{2}}{N}^{\frac{3}{4}-\gamma }$ from the convergents of the continued fraction expansion of $\frac{e}{N-\lceil \frac{a^2+b^2}{ab}\sqrt{N}\rceil +1}$ . The second part of the paper reports four cryptanalytic attacks on t instances of RSA moduli $N_s=p_s{q}_s$ for $s=1,2,\dots ,t$ where we use $N-\lceil \frac{a^2+b^2}{ab}\sqrt{N}\rceil +1$ as an approximation of $\varphi (N)$ satisfying generalized key equations of the shape $e_{s}d-k_s\varphi (N_s)=1$ , $e_s{d}_s-k\varphi (N_s)=1$ , $e_{s}d-k_s\varphi (N_s)=z_s$ , and $e_s{d}_s-k\varphi (N_s)=z_s$ for unknown positive integers $d,k_s,d_s,k_s$ , and $z_s$ , where we establish that t RSA moduli can be simultaneously factored in polynomial time using combinations of simultaneous Diophantine approximations and lattice basis reduction methods. In all the reported attacks, we have found an improved short secret exponent bound, which is considered to be better than some bounds as reported in the literature.

Since the first coronavirus disease 2019 (COVID-19) outbreak appeared in Wuhan, mainland China on December 31, 2019, the geographical spread of the epidemic was swift. Malaysia is one of the countries that were hit substantially by the outbreak, particularly in the second wave. This study aims to simulate the infectious trend and trajectory of COVID-19 to understand the severity of the disease and determine the approximate number of days required for the trend to decline. The number of confirmed positive infectious cases [as reported by Ministry of Health, Malaysia (MOH)] were used from January 25, 2020 to March 31, 2020. This study simulated the infectious count for the same duration to assess the predictive capability of the Susceptible-Infectious-Recovered (SIR) model. The same model was used to project the simulation trajectory of confirmed positive infectious cases for 80 days from the beginning of the outbreak and extended the trajectory for another 30 days to obtain an overall picture of the severity of the disease in Malaysia. The transmission rate, β also been utilized to predict the cumulative number of infectious individuals. Using the SIR model, the simulated infectious cases count obtained was not far from the actual count. The simulated trend was able to mimic the actual count and capture the actual spikes approximately. The infectious trajectory simulation for 80 days and the extended trajectory for 110 days depicts that the inclining trend has peaked and ended and will decline towards late April 2020. Furthermore, the predicted cumulative number of infectious individuals tallies with the preparations undertaken by the MOH. The simulation indicates the severity of COVID-19 disease in Malaysia, suggesting a peak of infectiousness in mid-March 2020 and a probable decline in late April 2020. Overall, the study findings indicate that outbreak control measures such as the Movement Control Order (MCO), social distancing and increased hygienic awareness is needed to control the transmission of the outbreak in Malaysia.