A method for detecting fraud attempts in a reverse vending machine, the reverse vending machine comprising at least one image sensor (24, 26) and at least one data processing unit, the method comprising: using said at least one image sensor (24, 26) to obtain at least two images of a returned item inserted into the reverse vending machine, said at least two images are obtained at at least two different points in time; using said at least one data processing unit to apply a first feature extraction algorithm to extract a first feature indicative of the presence and a relative position of a first object associated with said first feature from said at least two images obtained at at least two different points in time; using said at least one data processing unit to apply a second feature extraction algorithm which is different from said first feature extraction algorithm, to extract a second feature indicative of the presence and a relative position of a second object associated with said second feature from said at least two images obtained at at least two different points in time, said second object being different from said first object; and using said at least one data processing unit to determine whether a position or a motion of said first object is inconsistent with a position or-motion of said second object, and to calculate a confidence level of fraud based on that determination, said determining includes estimating relative motions or velocities for said first and said second object based on said relative positions and time intervals between said at least two different points in time, and indicating an inconsistent motion of said first object relative to said second object if there is a substantial difference between said estimated relative motions or said first and said second estimated velocity. using said at least one image sensor (24, 26) to obtain at least two images of a returned item inserted into the reverse vending machine, said at least two images are obtained at at least two different points in time; using said at least one data processing unit to apply a first feature extraction algorithm to extract a first feature indicative of the presence and a relative position of a first object associated with said first feature from said at least two images obtained at at least two different points in time; using said at least one data processing unit to apply a second feature extraction algorithm which is different from said first feature extraction algorithm, to extract a second feature indicative of the presence and a relative position of a second object associated with said second feature from said at least two images obtained at at least two different points in time, said second object being different from said first object; and using said at least one data processing unit to determine whether a position or a motion of said first object is inconsistent with a position or-motion of said second object, and to calculate a confidence level of fraud based on that determination, said determining includes estimating relative motions or velocities for said first and said second object based on said relative positions and time intervals between said at least two different points in time, and indicating an inconsistent motion of said first object relative to said second object if there is a substantial difference between said estimated relative motions or said first and said second estimated velocity.