The way people live has changed with digital appliances and mobile devices. Internet access has taken a huge leap forward for mankind. With all the big benefits, however, many weak points emerge and pose too many challenges. It is noticeable that in recent times the concealment of digital information has taken an important space in circles that require concealment of confidential data to transfer it. One of these methods most used is Steganography in images. Image Steganography can be done through various techniques, algorithms ,and tools. The most dangerous of all is the use of information-hiding technology to hide the malicious load in the images, and this is what many attackers do to do their malicious purposes on the victim’s de vice, and this movement is called Stegomalware. We reviewed ef ficient and robust steganalysis approaches based on traditional and deep learning, and we proposed an anti-stego-malware strategy that aims to destroy the Stegomalware content in the image to prevent the expected harmful effects while causing no visual degradation to the image. Our suggested approach is to add lightweight noise into the Stego images to destroy malicious payload. Experimental results showed that the proposed destruction technique is efficient and can destroying stegomalware content with minimum performance over head.